subject:(Markov chain Monte Carlo)

University of Southern California

1. Aslam, Kamran. A stochastic Markov chain approach for tennis: Monte Carlo simulation and modeling.

Degree: PhD, Aerospace Engineering, 2012, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/12697/rec/371

► This dissertation describes the computational formulation of probability density functions (pdfs) that facilitate head-to-head match simulations in tennis along with ranking systems developed from their… (more)

Subjects/Keywords: monte carlo; markov chain; tennis

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APA (6^th Edition):

Aslam, K. (2012). A stochastic Markov chain approach for tennis: Monte Carlo simulation and modeling. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/12697/rec/371

Chicago Manual of Style (16^th Edition):

Aslam, Kamran. “A stochastic Markov chain approach for tennis: Monte Carlo simulation and modeling.” 2012. Doctoral Dissertation, University of Southern California. Accessed February 26, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/12697/rec/371.

MLA Handbook (7^th Edition):

Aslam, Kamran. “A stochastic Markov chain approach for tennis: Monte Carlo simulation and modeling.” 2012. Web. 26 Feb 2021.

Vancouver:

Aslam K. A stochastic Markov chain approach for tennis: Monte Carlo simulation and modeling. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2021 Feb 26]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/12697/rec/371.

Council of Science Editors:

Aslam K. A stochastic Markov chain approach for tennis: Monte Carlo simulation and modeling. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/12697/rec/371

2. Auranen, Toni. Computational Methods for Bayesian Estimation of Neuromagnetic Sources.

Degree: 2007, Helsinki University of Technology

URL: http://lib.tkk.fi/Diss/2007/isbn9789512289547/

►

The electromagnetic inverse problem in human brain research consists of determining underlying source currents in the brain based on measurements outside the head. Solution to… (more)

▼

The electromagnetic inverse problem in human brain research consists of determining underlying source currents in the brain based on measurements outside the head. Solution to the inverse problem is ambiguous, necessitating the use of prior information and modeling assumptions for obtaining reasonable inverse estimates. In this study, we create new and improve existing computational methods for estimating neuromagnetic sources in the human brain. One straightforward way of incorporating presumptions to this problem is to formulate it in a probabilistic Bayesian manner. Bayesian statistics is largely based on modeling uncertainties associated with parameters constituting the model by representing them with probability distributions. In this work, existing neuroscientific knowledge and information from anatomical and functional magnetic resonance imaging are used as prior assumptions in model implementation. The neuromagnetic inverse problem is resolved with two different approaches. First, we perform the analysis using distributed source current modeling and infer some arbitrary parameter choices and the source currents from the measurement data by using numerical sampling methods. We apply similar strategies to cortically constrained current dipole localization and suggest using functional magnetic resonance imaging data for guiding the sampling algorithm. The models are tested with simulated and measured data. The presented methods are rather automatic, yielding plausible and robust inverse estimates of cortical current sources. With the spatiotemporal dipole localization model, the inclusion of functional magnetic resonance imaging data improves performance of the numerical sampling method. However, apparent multimodality of the parameter posterior distribution causes complications especially with empirical data. We suggest using loose cortical orientation constraints for smoothing down the complicated posterior distribution instead of marginal improvements to the sampling scheme. This might help to overcome the somewhat limited mixing properties of the sampling algorithm and ease the inconvenient multimodality of the posterior distribution.

Ihmisaivojen tutkimukseen liittyvällä sähkömagneettisella käänteisongelmalla tarkoitetaan aivojen virtalähteiden paikantamista pään ulkopuolisten mittausten perusteella. Ongelmaan ei ole yksikäsitteistä ratkaisua, joten mallintamisessa on käytettävä ennakko-oletuksia järkevien ratkaisujen tuottamiseksi. Tässä tutkimuksessa kehitämme uusia ja parannamme olemassaolevia laskennallisia menetelmiä aivoissa syntyvien magneettikenttiä tuottavien lähteiden paikantamiseksi. Kenties yksinkertaisin tapa lisätä ennakko-oletuksia tähän ongelmaan on käyttää bayesilaista mallintamista. Bayesilainen tilastotiede perustuu pitkälti parametrien epävarmuuksien mallintamiseen ja esittämiseen todennäköisyysjakaumin. Työn mallien muodostamisessa käytetään apuna aivojen toiminnallisesta ja rakenteellisesta magneettikuvauksesta saatavaa neurotieteellistä ennakkotietoa. Sähkömagneettisen…

Advisors/Committee Members: Helsinki University of Technology, Department of Electrical and Communications Engineering, Laboratory of Computational Engineering.

Subjects/Keywords: inverse problem; magnetoencephalography; Markov chain Monte Carlo

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APA (6^th Edition):

Auranen, T. (2007). Computational Methods for Bayesian Estimation of Neuromagnetic Sources. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2007/isbn9789512289547/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Auranen, Toni. “Computational Methods for Bayesian Estimation of Neuromagnetic Sources.” 2007. Thesis, Helsinki University of Technology. Accessed February 26, 2021. http://lib.tkk.fi/Diss/2007/isbn9789512289547/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Auranen, Toni. “Computational Methods for Bayesian Estimation of Neuromagnetic Sources.” 2007. Web. 26 Feb 2021.

Vancouver:

Auranen T. Computational Methods for Bayesian Estimation of Neuromagnetic Sources. [Internet] [Thesis]. Helsinki University of Technology; 2007. [cited 2021 Feb 26]. Available from: http://lib.tkk.fi/Diss/2007/isbn9789512289547/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Auranen T. Computational Methods for Bayesian Estimation of Neuromagnetic Sources. [Thesis]. Helsinki University of Technology; 2007. Available from: http://lib.tkk.fi/Diss/2007/isbn9789512289547/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rochester Institute of Technology

3. Dumont, Michael. Markov chain Monte Carlo on the GPU.

Degree: Computer Science (GCCIS), 2011, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/83

► Markov chains are a useful tool in statistics that allow us to sample and model a large population of individuals. We can extend this idea… (more)

Subjects/Keywords: Approximation; GPGPU; GPU; Markov chain; Monte carlo

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APA (6^th Edition):

Dumont, M. (2011). Markov chain Monte Carlo on the GPU. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/83

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Dumont, Michael. “Markov chain Monte Carlo on the GPU.” 2011. Thesis, Rochester Institute of Technology. Accessed February 26, 2021. https://scholarworks.rit.edu/theses/83.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Dumont, Michael. “Markov chain Monte Carlo on the GPU.” 2011. Web. 26 Feb 2021.

Vancouver:

Dumont M. Markov chain Monte Carlo on the GPU. [Internet] [Thesis]. Rochester Institute of Technology; 2011. [cited 2021 Feb 26]. Available from: https://scholarworks.rit.edu/theses/83.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dumont M. Markov chain Monte Carlo on the GPU. [Thesis]. Rochester Institute of Technology; 2011. Available from: https://scholarworks.rit.edu/theses/83

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Alberta

4. Habib, Shahnoor. A Combined Three-Phase Signal Extraction of the Sudbury Neutrino Observatory Data Using Markov Chain Monte Carlo Technique.

Degree: PhD, Department of Physics, 2011, University of Alberta

URL: https://era.library.ualberta.ca/files/cvx021f21f

► \begin{Large} \truedoublespacing \begin{center} bf{Abstract} \end{center} Neutrino physics has entered an era of precision, after proving that the Standard Solar Model is a viable theory and… (more)

▼ \begin{Large} \truedoublespacing \begin{center} bf{Abstract} \end{center} Neutrino physics has entered an era of precision, after proving that the Standard Solar Model is a viable theory and going beyond the current Standard Model of particle physics by proving that neutrinos possess nonzero masses. The Sudbury Neutrino Observatory (SNO) experiment, along with other experiments, has restricted neutrino mixing angle (θ₁₂) and the mass square difference (Δ m²₂₁) to lie within the large mixing solution area. SNO, located 2~km underground in Sudbury, Canada, was an ultraclean heavy-water (D₂O) imaging detector for observing neutrinos produced by fusion reactions in the Sun. Neutrino interactions with heavy water resulted in flashes of light called \breve{{C}}erenkov radiation which was detected by an array of photomultiplier tubes. SNO took data from November 1999 to November 2006, totalling 1082 days of data taking. This work describes an improved measurement of the mixing parameters from a combined fit of all the data. For the signal extraction fit on the data consisting of 4 observable of an event – radial position, recoil electron energy, direction relative to the Sun and event isotropy – Markov Chain Monte Carlo (MCMC) method based on Metropolis algorithm was employed. The nuisance parameters (systematics), weighted by external constraints, were allowed to vary in the fit. The goal of the thesis was to extract the survival probabilities of electron neutrinos and determine the total flux of active-flavour neutrinos from ⁸B decay in the Sun measured through the neutral current interactions of neutrinos on deuterium. The ⁸B flux from the fit is (5.24±0.02) × 10⁶~{cm}^-2~ s^-1; uncertainty from statistics and systematics is 3.56%. Along with ⁸B flux, the fit extracted energy spectra of charged current interactions of neutrinos on deuterium and elastic scattering interactions of neutrinos on electrons. The fit described the energy-dependent day survival probability of solar neutrinos as a quadratic equation and asymmetry on the day survival probability as a linear equation. Four polynomial coefficients of the survival probability were extracted from the fit: constant coefficient as 0.3206±0.0197, linear coefficient as 0.005±0.008 and quadratic coefficient as -0.0014±0.0033. There are two coefficients on the day-night asymmetry: constant coefficient as 0.0496±0.0347 and the linear coefficient as -0.018±0.028. The day-night asymmetry (0.0496) observed is 1.4σ away from zero. Using these findings, the oscillation space in terms of Δ m²₂₁ and θ₁₂ will be further constrained. Compared to the previous published SNO results, the uncertainty on ⁸B went down from 3.83% to 3.56% and average ⁸B ν_e survival probability (p₀) went down from 6.57% to 6.14%. If the data were analysed with the same assumptions, the decrease in uncertainties would have been approximately twice as big; however,…

Subjects/Keywords: neutrino physics; Markov Chain Monte Carlo

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Habib, S. (2011). A Combined Three-Phase Signal Extraction of the Sudbury Neutrino Observatory Data Using Markov Chain Monte Carlo Technique. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cvx021f21f

Chicago Manual of Style (16^th Edition):

Habib, Shahnoor. “A Combined Three-Phase Signal Extraction of the Sudbury Neutrino Observatory Data Using Markov Chain Monte Carlo Technique.” 2011. Doctoral Dissertation, University of Alberta. Accessed February 26, 2021. https://era.library.ualberta.ca/files/cvx021f21f.

MLA Handbook (7^th Edition):

Habib, Shahnoor. “A Combined Three-Phase Signal Extraction of the Sudbury Neutrino Observatory Data Using Markov Chain Monte Carlo Technique.” 2011. Web. 26 Feb 2021.

Vancouver:

Habib S. A Combined Three-Phase Signal Extraction of the Sudbury Neutrino Observatory Data Using Markov Chain Monte Carlo Technique. [Internet] [Doctoral dissertation]. University of Alberta; 2011. [cited 2021 Feb 26]. Available from: https://era.library.ualberta.ca/files/cvx021f21f.

Council of Science Editors:

Habib S. A Combined Three-Phase Signal Extraction of the Sudbury Neutrino Observatory Data Using Markov Chain Monte Carlo Technique. [Doctoral Dissertation]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/cvx021f21f

University of Alberta

5. Howard, Christopher William. A search for hep neutrinos with the Sudbury Neutrino Observatory.

Degree: PhD, Department of Physics, 2010, University of Alberta

URL: https://era.library.ualberta.ca/files/v118rd64z

► This thesis focuses on the search for neutrinos from the solar hep reaction using the combined three phases of the Sudbury Neutrino Observatory (SNO) data.… (more)

Subjects/Keywords: neutrino; solar model; markov chain monte carlo

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Howard, C. W. (2010). A search for hep neutrinos with the Sudbury Neutrino Observatory. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/v118rd64z

Chicago Manual of Style (16^th Edition):

Howard, Christopher William. “A search for hep neutrinos with the Sudbury Neutrino Observatory.” 2010. Doctoral Dissertation, University of Alberta. Accessed February 26, 2021. https://era.library.ualberta.ca/files/v118rd64z.

MLA Handbook (7^th Edition):

Howard, Christopher William. “A search for hep neutrinos with the Sudbury Neutrino Observatory.” 2010. Web. 26 Feb 2021.

Vancouver:

Howard CW. A search for hep neutrinos with the Sudbury Neutrino Observatory. [Internet] [Doctoral dissertation]. University of Alberta; 2010. [cited 2021 Feb 26]. Available from: https://era.library.ualberta.ca/files/v118rd64z.

Council of Science Editors:

Howard CW. A search for hep neutrinos with the Sudbury Neutrino Observatory. [Doctoral Dissertation]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/v118rd64z

University of Waterloo

6. Mathew, Manoj. The Application of Markov Chain Monte Carlo Techniques in Non-Linear Parameter Estimation for Chemical Engineering Models.

Degree: 2013, University of Waterloo

URL: http://hdl.handle.net/10012/7906

► Modeling of chemical engineering systems often necessitates using non-linear models. These models can range in complexity, from a simple analytical equation to a system of… (more)

▼ Modeling of chemical engineering systems often necessitates using non-linear models. These models can range in complexity, from a simple analytical equation to a system of differential equations. Regardless of what type of model is being utilized, determining parameter estimates is essential in everyday chemical engineering practice. One promising approach to non-linear regression is a technique called Markov Chain Monte Carlo (MCMC).This method produces reliable parameter estimates and generates joint confidence regions (JCRs) with correct shape and correct probability content. Despite these advantages, its application in chemical engineering literature has been limited. Therefore, in this project, MCMC methods were applied to a variety of chemical engineering models. The objectives of this research is to (1) illustrate how to implement MCMC methods in complex non-linear models (2) show the advantages of using MCMC techniques over classical regression approaches and (3) provide practical guidelines on how to reduce the computational time. MCMC methods were first applied to the biological oxygen demand (BOD) problem. In this case study, an implementation procedure was outlined using specific examples from the BOD problem. The results from the study illustrated the importance of estimating the pure error variance as a parameter rather than fixing its value based on the mean square error. In addition, a comparison was carried out between the MCMC results and the results obtained from using classical regression approaches. The findings show that although similar point estimates are obtained, JCRs generated from approximation methods cannot model the parameter uncertainty adequately. Markov Chain Monte Carlo techniques were then applied in estimating reactivity ratios in the Mayo-Lewis model, Meyer-Lowry model, the direct numerical integration model and the triad fraction multiresponse model. The implementation steps for each of these models were discussed in detail and the results from this research were once again compared to previously used approximation methods. Once again, the conclusion drawn from this work showed that MCMC methods must be employed in order to obtain JCRs with the correct shape and correct probability content. MCMC methods were also applied in estimating kinetic parameter used in the solid oxide fuel cell study. More specifically, the kinetics of the water-gas shift reaction, which is used in generating hydrogen for the fuel cell, was studied. The results from this case study showed how the MCMC output can be analyzed in order to diagnose parameter observability and correlation. A significant portion of the model needed to be reduced due to these issues of observability and correlation. Point estimates and JCRs were then generated using the reduced model and diagnostic checks were carried out in order to ensure the model was able to capture the data adequately. A few select parameters in the Waterloo Polymer Simulator were estimated using the MCMC algorithm. Previous studies…

Subjects/Keywords: Markov Chain Monte Carlo; Parameter Estimation

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APA (6^th Edition):

Mathew, M. (2013). The Application of Markov Chain Monte Carlo Techniques in Non-Linear Parameter Estimation for Chemical Engineering Models. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/7906

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mathew, Manoj. “The Application of Markov Chain Monte Carlo Techniques in Non-Linear Parameter Estimation for Chemical Engineering Models.” 2013. Thesis, University of Waterloo. Accessed February 26, 2021. http://hdl.handle.net/10012/7906.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mathew, Manoj. “The Application of Markov Chain Monte Carlo Techniques in Non-Linear Parameter Estimation for Chemical Engineering Models.” 2013. Web. 26 Feb 2021.

Vancouver:

Mathew M. The Application of Markov Chain Monte Carlo Techniques in Non-Linear Parameter Estimation for Chemical Engineering Models. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10012/7906.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mathew M. The Application of Markov Chain Monte Carlo Techniques in Non-Linear Parameter Estimation for Chemical Engineering Models. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/7906

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Ontario Institute of Technology

7. McDougall, Robin. Robotic radiation mapping using modelling and probabilistic analysis of sparse data.

Degree: 2015, University of Ontario Institute of Technology

URL: http://hdl.handle.net/10155/552

► An approach for generating radiation intensity maps, comprised of a mobile robotic platform and an integrated radiation model, is presented, and its ability to generate… (more)

Subjects/Keywords: Radiation mapping; Robotics; Markov Chain Monte Carlo

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APA (6^th Edition):

McDougall, R. (2015). Robotic radiation mapping using modelling and probabilistic analysis of sparse data. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/552

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

McDougall, Robin. “Robotic radiation mapping using modelling and probabilistic analysis of sparse data.” 2015. Thesis, University of Ontario Institute of Technology. Accessed February 26, 2021. http://hdl.handle.net/10155/552.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

McDougall, Robin. “Robotic radiation mapping using modelling and probabilistic analysis of sparse data.” 2015. Web. 26 Feb 2021.

Vancouver:

McDougall R. Robotic radiation mapping using modelling and probabilistic analysis of sparse data. [Internet] [Thesis]. University of Ontario Institute of Technology; 2015. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10155/552.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

McDougall R. Robotic radiation mapping using modelling and probabilistic analysis of sparse data. [Thesis]. University of Ontario Institute of Technology; 2015. Available from: http://hdl.handle.net/10155/552

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Leiden University

8. Kwakernaak, Lennard. A Modified Frenkel-Kontorova Model for Sampling Nucleosomal DNA Sequences and Positions.

Degree: 2020, Leiden University

URL: http://hdl.handle.net/1887/137885

► The DNA in eukaryotic organisms is largely stored in a compact wrap around histone proteins to form nucleosomes. The mechanics of the DNA play a… (more)

Subjects/Keywords: DNA; Monte Carlo; Markov Chain; Sequence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kwakernaak, L. (2020). A Modified Frenkel-Kontorova Model for Sampling Nucleosomal DNA Sequences and Positions. (Masters Thesis). Leiden University. Retrieved from http://hdl.handle.net/1887/137885

Chicago Manual of Style (16^th Edition):

Kwakernaak, Lennard. “A Modified Frenkel-Kontorova Model for Sampling Nucleosomal DNA Sequences and Positions.” 2020. Masters Thesis, Leiden University. Accessed February 26, 2021. http://hdl.handle.net/1887/137885.

MLA Handbook (7^th Edition):

Kwakernaak, Lennard. “A Modified Frenkel-Kontorova Model for Sampling Nucleosomal DNA Sequences and Positions.” 2020. Web. 26 Feb 2021.

Vancouver:

Kwakernaak L. A Modified Frenkel-Kontorova Model for Sampling Nucleosomal DNA Sequences and Positions. [Internet] [Masters thesis]. Leiden University; 2020. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1887/137885.

Council of Science Editors:

Kwakernaak L. A Modified Frenkel-Kontorova Model for Sampling Nucleosomal DNA Sequences and Positions. [Masters Thesis]. Leiden University; 2020. Available from: http://hdl.handle.net/1887/137885

University of Melbourne

9. Vu, Tuyet Thi Anh. A Particle Markov Chain Monte Carlo algorithm for random finite set based multi-target tracking.

Degree: 2011, University of Melbourne

URL: http://hdl.handle.net/11343/36875

► The multi target tracking (MTT) problem is essentially that of estimating the presence and associated time trajectories of moving objects based on measurements from a… (more)

▼ The multi target tracking (MTT) problem is essentially that of estimating the presence and associated time trajectories of moving objects based on measurements from a variety of sensors. Tracking a large number of unknown targets which move close and cross each other such as biological cells becomes difficult. The targets being tracked may randomly appear and disappear from the field of view, they may be temporarily obscured by other objects, may merge and split, may spawn other targets, and may cross or travel very close to each other for extended periods of time. Sensor measurements also present a number of challenging characteristics, such as noise which introduces location errors and may cause missed detection of targets, false measurements which do not belong to a valid target of interest, ghosting, misidentification etc. A new approach to this problem is proposed by first formulating the problem in a random set finite framework and then using the Particle Markov Chain Monte Carlo (PMCMC) method for solving the problem. Under the random finite set (RFS) framework originally proposed by Mahler, a multi-target posterior distribution is propagated recursively via a Bayesian framework. The intractability of the posterior distribution is computed by using the PMCMC method that uses the sequential Monte Carlo outputs for the Markov Chain Monte Carlo (MCMC) method. A RFS is a finite-set valued random variable. Alternatively, RFS can be interpreted as a random variable that is random in number of elements and in the values of these elements themselves and that the order of its elements is irrelevant. As a result, the RFS framework is a mathematically rigorous tool for capturing all uncertainties of its elements and its cardinality. With the uncertain properties of the MTT problem, the RFS framework is naturally used to formulate the MTT problem to capture the essence of MTT problem and then allows the multi-target posterior distribution to be propagated via a Bayesian framework. The first contribution of this dissertation is to derive the posterior distribution for the trajectories of the targets that is the special case for the multi-target posterior distribution. The multi-target posterior distribution is intractable so an approximation method such as PMCMC is required. PMCMC methods proposed by use the Sequential Monte Carlo (SMC) algorithm to design an efficient high dimensional proposal distribution for the Markov Chain Monte Carlo (MCMC) method. The premise of this method is to sample from any distribution which has no closed form solution and which applying the traditional MCMC method or SMC method fails to give a reliable solution or is unfeasible on its own. The second contribution is to derive a RFS based PMCMC algorithm and implement this algorithm for the multi-target tracking problem when targets move close and/or cross each other in a dense environment and the number of targets is unknown.

Subjects/Keywords: multi-target tracking; Particle Markov Chain Monte Carlo; Markov Chain Monte Carlo; random sets; Sequential Monte Carlo

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vu, T. T. A. (2011). A Particle Markov Chain Monte Carlo algorithm for random finite set based multi-target tracking. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/36875

Chicago Manual of Style (16^th Edition):

Vu, Tuyet Thi Anh. “A Particle Markov Chain Monte Carlo algorithm for random finite set based multi-target tracking.” 2011. Doctoral Dissertation, University of Melbourne. Accessed February 26, 2021. http://hdl.handle.net/11343/36875.

MLA Handbook (7^th Edition):

Vu, Tuyet Thi Anh. “A Particle Markov Chain Monte Carlo algorithm for random finite set based multi-target tracking.” 2011. Web. 26 Feb 2021.

Vancouver:

Vu TTA. A Particle Markov Chain Monte Carlo algorithm for random finite set based multi-target tracking. [Internet] [Doctoral dissertation]. University of Melbourne; 2011. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/11343/36875.

Council of Science Editors:

Vu TTA. A Particle Markov Chain Monte Carlo algorithm for random finite set based multi-target tracking. [Doctoral Dissertation]. University of Melbourne; 2011. Available from: http://hdl.handle.net/11343/36875

10. Santos, Tássio Naia dos. Grafos aleatórios exponenciais.

Degree: Mestrado, Ciência da Computação, 2013, University of São Paulo

URL: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-19022014-195734/ ;

►

Estudamos o comportamento da familia aresta-triangulo de grafos aleatorios exponenciais (ERG) usando metodos de Monte Carlo baseados em Cadeias de Markov. Comparamos contagens de subgrafos… (more)

Subjects/Keywords: Cadeia de Markov; Combinatória; Combinatorics; Grafos Aleatórios; Markov Chain; Monte Carlo; Monte Carlo; Random Graphs

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APA (6^th Edition):

Santos, T. N. d. (2013). Grafos aleatórios exponenciais. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/45/45134/tde-19022014-195734/ ;

Chicago Manual of Style (16^th Edition):

Santos, Tássio Naia dos. “Grafos aleatórios exponenciais.” 2013. Masters Thesis, University of São Paulo. Accessed February 26, 2021. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-19022014-195734/ ;.

MLA Handbook (7^th Edition):

Santos, Tássio Naia dos. “Grafos aleatórios exponenciais.” 2013. Web. 26 Feb 2021.

Vancouver:

Santos TNd. Grafos aleatórios exponenciais. [Internet] [Masters thesis]. University of São Paulo; 2013. [cited 2021 Feb 26]. Available from: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-19022014-195734/ ;.

Council of Science Editors:

Santos TNd. Grafos aleatórios exponenciais. [Masters Thesis]. University of São Paulo; 2013. Available from: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-19022014-195734/ ;

The Ohio State University

11. Olsen, Andrew Nolan. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.

Degree: PhD, Statistics, 2015, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406

► Markov chains are an incredibly powerful tool for statisticians and other practitioners. They allow for random draws, though autocorrelated, to be obtained from a vast… (more)

▼ Markov chains are an incredibly powerful tool for statisticians and other practitioners. They allow for random draws, though autocorrelated, to be obtained from a vast array of target distributions, even when the distribution is known only up to a constant. These draws may then be used to answer key questions of interest. Markov chains are used in many settings and are the predominant method for performing inference for Bayesian methods. The utility of Markov chains lies largely in the simplicity with which they are implemented. The most basic algorithms are easily understood and are not challenging to program. The trade-off with ease of implementation, however, is that issues with Markov chains, particularly with respect to convergence, can occasionally be left undiagnosed. For example, a Markov chain may not have been run long enough to accurately capture the features of the distribution of interest, or perhaps the error of the resulting estimates is grossly underrepresented, if it is considered at all.The study of Markov chain convergence can be summarized by two main questions:Question 1: Was the simulation run long enough? Question 2: How accurate are the resulting estimates?While simple and clear, these questions are often left unanswered when Markov chain Monte Carlo methods are implemented. This is largely due to the fact that these answers require theoretical analysis of the convergence of the Markov chain, which can be challenging. This dissertation discusses the theory of Markov chains and their convergence, including how to rigorously answer Question 1 and Question 2. A variety of methods are available, and several are illustrated with examples.One approach answers Question 1 by obtaining draws that approximate the target distribution closely. Markov chains may then be started from these draws, resulting in immediate closeness to the target distribution. Several algorithms for accomplishing this are introduced and developed. Results are provided which quantify the quality of the approximations. A comparison of the efficiency of the algorithms is also provided. Another approach is the formal establishment of convergence rates. Once these are established, one method to answer Question 1 is to compute the number of iterations required so that the ultimate distribution obtained is close to the target distribution. This approach is also illustrated with examples.A final approach is to compute standard errors of the resulting estimates, which directly answers Question 2. Question 1, however, is also answered because when estimates are accurate enough, the chain has been run for a sufficient duration. This is similarly illustrated with examples.Bayesian scale-usage models are used to analyze surveys where individual respondents differ in their use of a rating scale. The convergence rate theory for these models, which guarantees answers to Question 1 and Question 2, is fully established. The methods are then extended to a setting where demographics can govern the way in which respondents differ in their answer… Advisors/Committee Members: Herbei, Radu (Advisor).

Subjects/Keywords: Statistics; Markov chain Monte Carlo convergence; Markov chain Monte Carlo standard errors; geometric ergodicity; scale-usage heterogeneity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Olsen, A. N. (2015). When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406

Chicago Manual of Style (16^th Edition):

Olsen, Andrew Nolan. “When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.” 2015. Doctoral Dissertation, The Ohio State University. Accessed February 26, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406.

MLA Handbook (7^th Edition):

Olsen, Andrew Nolan. “When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.” 2015. Web. 26 Feb 2021.

Vancouver:

Olsen AN. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2021 Feb 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406.

Council of Science Editors:

Olsen AN. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406

University of Edinburgh

12. Graham, Matthew McKenzie. Auxiliary variable Markov chain Monte Carlo methods.

Degree: PhD, 2018, University of Edinburgh

URL: http://hdl.handle.net/1842/28962

► Markov chain Monte Carlo (MCMC) methods are a widely applicable class of algorithms for estimating integrals in statistical inference problems. A common approach in MCMC… (more)

▼ Markov chain Monte Carlo (MCMC) methods are a widely applicable class of algorithms for estimating integrals in statistical inference problems. A common approach in MCMC methods is to introduce additional auxiliary variables into the Markov chain state and perform transitions in the joint space of target and auxiliary variables. In this thesis we consider novel methods for using auxiliary variables within MCMC methods to allow approximate inference in otherwise intractable models and to improve sampling performance in models exhibiting challenging properties such as multimodality. We first consider the pseudo-marginal framework. This extends the Metropolis–Hastings algorithm to cases where we only have access to an unbiased estimator of the density of target distribution. The resulting chains can sometimes show ‘sticking’ behaviour where long series of proposed updates are rejected. Further the algorithms can be difficult to tune and it is not immediately clear how to generalise the approach to alternative transition operators. We show that if the auxiliary variables used in the density estimator are included in the chain state it is possible to use new transition operators such as those based on slice-sampling algorithms within a pseudo-marginal setting. This auxiliary pseudo-marginal approach leads to easier to tune methods and is often able to improve sampling efficiency over existing approaches. As a second contribution we consider inference in probabilistic models defined via a generative process with the probability density of the outputs of this process only implicitly defined. The approximate Bayesian computation (ABC) framework allows inference in such models when conditioning on the values of observed model variables by making the approximation that generated observed variables are ‘close’ rather than exactly equal to observed data. Although making the inference problem more tractable, the approximation error introduced in ABC methods can be difficult to quantify and standard algorithms tend to perform poorly when conditioning on high dimensional observations. This often requires further approximation by reducing the observations to lower dimensional summary statistics. We show how including all of the random variables used in generating model outputs as auxiliary variables in a Markov chain state can allow the use of more efficient and robust MCMC methods such as slice sampling and Hamiltonian Monte Carlo (HMC) within an ABC framework. In some cases this can allow inference when conditioning on the full set of observed values when standard ABC methods require reduction to lower dimensional summaries for tractability. Further we introduce a novel constrained HMC method for performing inference in a restricted class of differentiable generative models which allows conditioning the generated observed variables to be arbitrarily close to observed data while maintaining computational tractability. As a final topicwe consider the use of an auxiliary temperature variable in MCMC methods to improve exploration of…

Subjects/Keywords: probability theory; Markov chain Monte Carlo algorithm; Metropolis–Hastings algorithm; algorithms; Hamiltonian Monte Carlo

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Graham, M. M. (2018). Auxiliary variable Markov chain Monte Carlo methods. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/28962

Chicago Manual of Style (16^th Edition):

Graham, Matthew McKenzie. “Auxiliary variable Markov chain Monte Carlo methods.” 2018. Doctoral Dissertation, University of Edinburgh. Accessed February 26, 2021. http://hdl.handle.net/1842/28962.

MLA Handbook (7^th Edition):

Graham, Matthew McKenzie. “Auxiliary variable Markov chain Monte Carlo methods.” 2018. Web. 26 Feb 2021.

Vancouver:

Graham MM. Auxiliary variable Markov chain Monte Carlo methods. [Internet] [Doctoral dissertation]. University of Edinburgh; 2018. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1842/28962.

Council of Science Editors:

Graham MM. Auxiliary variable Markov chain Monte Carlo methods. [Doctoral Dissertation]. University of Edinburgh; 2018. Available from: http://hdl.handle.net/1842/28962

13. Minvielle-Larrousse, Pierre. Méthodes de simulation stochastique pour le traitement de l’information : Stochastic simulation methods for information processing.

Degree: Docteur es, Mathématiques, 2019, Pau

URL: http://www.theses.fr/2019PAUU3005

►

Lorsqu’une grandeur d’intérêt ne peut être directement mesurée, il est fréquent de procéder à l’observation d’autres quantités qui lui sont liées par des lois physiques.… (more)

▼

Lorsqu’une grandeur d’intérêt ne peut être directement mesurée, il est fréquent de procéder à l’observation d’autres quantités qui lui sont liées par des lois physiques. Ces quantités peuvent contenir de l’information sur la grandeur d’intérêt si l’on sait résoudre le problème inverse, souvent mal posé, et inférer la valeur. L’inférence bayésienne constitue un outil statistique puissant pour l’inversion, qui requiert le calcul d’intégrales en grande dimension. Les méthodes Monte Carlo séquentielles (SMC), aussi dénommées méthodes particulaires, sont une classe de méthodes Monte Carlo permettant d’échantillonner selon une séquence de densités de probabilité de dimension croissante. Il existe de nombreuses applications, que ce soit en filtrage, en optimisation globale ou en simulation d’évènement rare. Les travaux ont porté notamment sur l’extension des méthodes SMC dans un contexte dynamique où le système, régi par un processus de Markov caché, est aussi déterminé par des paramètres statiques que l’on cherche à estimer. En estimation bayésienne séquentielle, la détermination de paramètres fixes provoque des difficultés particulières : un tel processus est non-ergodique, le système n’oubliant pas ses conditions initiales. Il est montré comment il est possible de surmonter ces difficultés dans une application de poursuite et identification de formes géométriques par caméra numérique CCD. Des étapes d’échantillonnage MCMC (Chaîne de Markov Monte Carlo) sont introduites pour diversifier les échantillons sans altérer la distribution a posteriori. Pour une autre application de contrôle de matériau, qui cette fois « hors ligne » mêle paramètres statiques et dynamiques, on a proposé une approche originale. Elle consiste en un algorithme PMMH (Particle Marginal Metropolis-Hastings) intégrant des traitements SMC Rao-Blackwellisés, basés sur des filtres de Kalman d’ensemble en interaction.D’autres travaux en traitement de l’information ont été menés, que ce soit en filtrage particulaire pour la poursuite d’un véhicule en phase de rentrée atmosphérique, en imagerie radar 3D par régularisation parcimonieuse ou en recalage d’image par information mutuelle.

When a quantity of interest is not directly observed, it is usual to observe other quantities that are linked by physical laws. They can provide information about the quantity of interest if it is able to solve the inverse problem, often ill posed, and infer the value. Bayesian inference is a powerful tool for inversion that requires the computation of high dimensional integrals. Sequential Monte Carlo (SMC) methods, a.k.a. interacting particles methods, are a type of Monte Carlo methods that are able to sample from a sequence of probability densities of growing dimension. They are many applications, for instance in filtering, in global optimization or rare event simulation.The work has focused in particular on the extension of SMC methods in a dynamic context where the system, governed by a hidden Markov process, is also determined by static parameters that we seek to…

Advisors/Committee Members: Tordeux, Sébastien (thesis director).

Subjects/Keywords: Statistiques appliquées; Problème inverse; Monte Carlo séquentiel; Chaîne de Markov Monte Carlo; Applied statistics; Inverse problem; Sequential Monte Carlo; Markov Chain Monte Carlo; 510

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Minvielle-Larrousse, P. (2019). Méthodes de simulation stochastique pour le traitement de l’information : Stochastic simulation methods for information processing. (Doctoral Dissertation). Pau. Retrieved from http://www.theses.fr/2019PAUU3005

Chicago Manual of Style (16^th Edition):

Minvielle-Larrousse, Pierre. “Méthodes de simulation stochastique pour le traitement de l’information : Stochastic simulation methods for information processing.” 2019. Doctoral Dissertation, Pau. Accessed February 26, 2021. http://www.theses.fr/2019PAUU3005.

MLA Handbook (7^th Edition):

Minvielle-Larrousse, Pierre. “Méthodes de simulation stochastique pour le traitement de l’information : Stochastic simulation methods for information processing.” 2019. Web. 26 Feb 2021.

Vancouver:

Minvielle-Larrousse P. Méthodes de simulation stochastique pour le traitement de l’information : Stochastic simulation methods for information processing. [Internet] [Doctoral dissertation]. Pau; 2019. [cited 2021 Feb 26]. Available from: http://www.theses.fr/2019PAUU3005.

Council of Science Editors:

Minvielle-Larrousse P. Méthodes de simulation stochastique pour le traitement de l’information : Stochastic simulation methods for information processing. [Doctoral Dissertation]. Pau; 2019. Available from: http://www.theses.fr/2019PAUU3005

University of Otago

14. Ma, Erfang. Application of Markov Chain Monte Carlo Methods in Electrical Impedance Tomography .

Degree: 2013, University of Otago

URL: http://hdl.handle.net/10523/4045

► This thesis discusses the application of Markov chain Monte Carlo (MCMC) methods in electrical impedance tomography (EIT). This topic arises in the Bayesian approach to… (more)

▼ This thesis discusses the application of Markov chain Monte Carlo (MCMC) methods in electrical impedance tomography (EIT). This topic arises in the Bayesian approach to the reconstruction problem of EIT. This study is a derivative of the work of Higdon et al. (2011). First, the computation of a forward map in EIT is discussed, since this computation is very important for the application of MCMC methods in EIT. The computation involves solving a boundary value problem using the finite element method (FEM). The convergence of the FEM approximation is studied numerically, and compelling evidence is provided for the validity of this approximation. Numerical experiments have shown that updating the Cholesky factor of stiffness matrix in FEM, can make the forward map computation much more efficient. The approximate computation of forward map is also discussed. This approximation uses a local linearisation of forward map, and is useful in making efficient MCMC proposals. The accuracy of this approximation is improved dramatically after a log-transformation on the variable of conductivity is introduced into this approximation. The improvement on the accuracy is later on shown to be crucial for this approximation to be employed in making an efficient proposal. Second, this thesis extends the work of Higdon et al. (2011). The same EIT model as in Higdon et al. (2011) is considered here. As in Higdon et al. (2011), a Bayesian approach is employed to reconstruct the conductivity distribution. The resulting posterior distribution is sampled by single site Metropolis, Random walk Metropolis (RWM), differential evolution MCMC (DE-MCMC), and Directional Random Walk Metropolis (DRWM). Our results on the performance of these MCMC algorithms are generally consistent with those of Higdon et al. (2011). Single site Metropolis, RWM and DRWM have also been analysed on sampling a high-dimensional Gaussian distribution. For their performance, elegant formulae for the acceptance rate for these MCMC samplers are obtained. These formulae indicate these algorithms perform similarly in this case as well, thus provide valuable insights into these MCMC algorithms. Finally and most importantly, this study comes up with some novel MCMC algorithms that dramatically outperform the standard single site Metropolis as in Higdon et al. (2011), for sampling the posterior distribution in EIT. According to Higdon et al. (2011), the performance of standard single site Metropolis was hard to surpass for this application. However, some of the novel MCMC algorithms in this thesis, when equipped with efficient computation of forward map, can be as many as 100 times more efficient than the standard single site Metropolis. These novel algorithms could be employed to explore posterior distribution of similar kind in other applications, e.g., electrical capacitance tomography. The success of these algorithms in this thesis indicates that they would also achieve equal success there. Advisors/Committee Members: Fox, Colin (advisor).

Subjects/Keywords: Markov chain Monte Carlo; electrical impedance tomography; inverse problems; Bayesian inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ma, E. (2013). Application of Markov Chain Monte Carlo Methods in Electrical Impedance Tomography . (Doctoral Dissertation). University of Otago. Retrieved from http://hdl.handle.net/10523/4045

Chicago Manual of Style (16^th Edition):

Ma, Erfang. “Application of Markov Chain Monte Carlo Methods in Electrical Impedance Tomography .” 2013. Doctoral Dissertation, University of Otago. Accessed February 26, 2021. http://hdl.handle.net/10523/4045.

MLA Handbook (7^th Edition):

Ma, Erfang. “Application of Markov Chain Monte Carlo Methods in Electrical Impedance Tomography .” 2013. Web. 26 Feb 2021.

Vancouver:

Ma E. Application of Markov Chain Monte Carlo Methods in Electrical Impedance Tomography . [Internet] [Doctoral dissertation]. University of Otago; 2013. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10523/4045.

Council of Science Editors:

Ma E. Application of Markov Chain Monte Carlo Methods in Electrical Impedance Tomography . [Doctoral Dissertation]. University of Otago; 2013. Available from: http://hdl.handle.net/10523/4045

15. Araki, Takamitsu. Adaptive Markov chain Monte Carlo for auxiliary variable method and its applications : 補助変数法に対する適応的マルコフ連鎖モンテカルロ法とその応用; ホジョヘンスウホウニタイスルテキオウテキマルコフレンサモンテカルロホウトソノオウヨウ.

Degree: 博士(工学), Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/9190

Subjects/Keywords: Markov chain Monte Carlo methods

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APA (6^th Edition):

Araki, T. (n.d.). Adaptive Markov chain Monte Carlo for auxiliary variable method and its applications : 補助変数法に対する適応的マルコフ連鎖モンテカルロ法とその応用; ホジョヘンスウホウニタイスルテキオウテキマルコフレンサモンテカルロホウトソノオウヨウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/9190

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Araki, Takamitsu. “Adaptive Markov chain Monte Carlo for auxiliary variable method and its applications : 補助変数法に対する適応的マルコフ連鎖モンテカルロ法とその応用; ホジョヘンスウホウニタイスルテキオウテキマルコフレンサモンテカルロホウトソノオウヨウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 26, 2021. http://hdl.handle.net/10061/9190.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Araki, Takamitsu. “Adaptive Markov chain Monte Carlo for auxiliary variable method and its applications : 補助変数法に対する適応的マルコフ連鎖モンテカルロ法とその応用; ホジョヘンスウホウニタイスルテキオウテキマルコフレンサモンテカルロホウトソノオウヨウ.” Web. 26 Feb 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Araki T. Adaptive Markov chain Monte Carlo for auxiliary variable method and its applications : 補助変数法に対する適応的マルコフ連鎖モンテカルロ法とその応用; ホジョヘンスウホウニタイスルテキオウテキマルコフレンサモンテカルロホウトソノオウヨウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10061/9190.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Araki T. Adaptive Markov chain Monte Carlo for auxiliary variable method and its applications : 補助変数法に対する適応的マルコフ連鎖モンテカルロ法とその応用; ホジョヘンスウホウニタイスルテキオウテキマルコフレンサモンテカルロホウトソノオウヨウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/9190

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

16. Chernoukhov, Alexander. Bayesian Spatial Additive Hazard Model.

Degree: MS, Mathematics and Statistics, 2013, National Library of Canada

URL: http://scholar.uwindsor.ca/etd/4965

► This thesis will be dealing with the problem of Bayesian estimation in additive survival data models accounting for spatial dependencies. We consider the Aalen's… (more)

Subjects/Keywords: Pure sciences; Additive hazard; Bayesian; Markov chain monte carlo; Spatial; Survival

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APA (6^th Edition):

Chernoukhov, A. (2013). Bayesian Spatial Additive Hazard Model. (Masters Thesis). National Library of Canada. Retrieved from http://scholar.uwindsor.ca/etd/4965

Chicago Manual of Style (16^th Edition):

Chernoukhov, Alexander. “Bayesian Spatial Additive Hazard Model.” 2013. Masters Thesis, National Library of Canada. Accessed February 26, 2021. http://scholar.uwindsor.ca/etd/4965.

MLA Handbook (7^th Edition):

Chernoukhov, Alexander. “Bayesian Spatial Additive Hazard Model.” 2013. Web. 26 Feb 2021.

Vancouver:

Chernoukhov A. Bayesian Spatial Additive Hazard Model. [Internet] [Masters thesis]. National Library of Canada; 2013. [cited 2021 Feb 26]. Available from: http://scholar.uwindsor.ca/etd/4965.

Council of Science Editors:

Chernoukhov A. Bayesian Spatial Additive Hazard Model. [Masters Thesis]. National Library of Canada; 2013. Available from: http://scholar.uwindsor.ca/etd/4965

Carnegie Mellon University

17. Potter, Christopher C. J. Kernel Selection for Convergence and Efficiency in Markov Chain Monte Carol.

Degree: 2013, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/249

► Markov Chain Monte Carlo (MCMC) is a technique for sampling from a target probability distribution, and has risen in importance as faster computing hardware has… (more)

▼ Markov Chain Monte Carlo (MCMC) is a technique for sampling from a target probability distribution, and has risen in importance as faster computing hardware has made possible the exploration of hitherto difficult distributions. Unfortunately, this powerful technique is often misapplied by poor selection of transition kernel for the Markov chain that is generated by the simulation. Some kernels are used without being checked against the convergence requirements for MCMC (total balance and ergodicity), but in this work we prove the existence of a simple proxy for total balance that is not as demanding as detailed balance, the most widely used standard. We show that, for discrete-state MCMC, that if a transition kernel is equivalent when it is “reversed” and applied to data which is also “reversed”, then it satisfies total balance. We go on to prove that the sequential single-variable update Metropolis kernel, where variables are simply updated in order, does indeed satisfy total balance for many discrete target distributions, such as the Ising model with uniform exchange constant. Also, two well-known papers by Gelman, Roberts, and Gilks (GRG)[1, 2] have proposed the application of the results of an interesting mathematical proof to the realistic optimization of Markov Chain Monte Carlo computer simulations. In particular, they advocated tuning the simulation parameters to select an acceptance ratio of 0.234 . In this paper, we point out that although the proof is valid, its result’s application to practical computations is not advisable, as the simulation algorithm considered in the proof is so inefficient that it produces very poor results under all circumstances. The algorithm used by Gelman, Roberts, and Gilks is also shown to introduce subtle time-dependent correlations into the simulation of intrinsically independent variables. These correlations are of particular interest since they will be present in all simulations that use multi-dimensional MCMC moves.

Subjects/Keywords: Markov Chain Monte Carlo; detailed balance; acceptance ratio; Metropolis algorithm; Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Potter, C. C. J. (2013). Kernel Selection for Convergence and Efficiency in Markov Chain Monte Carol. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/249

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Potter, Christopher C J. “Kernel Selection for Convergence and Efficiency in Markov Chain Monte Carol.” 2013. Thesis, Carnegie Mellon University. Accessed February 26, 2021. http://repository.cmu.edu/dissertations/249.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Potter, Christopher C J. “Kernel Selection for Convergence and Efficiency in Markov Chain Monte Carol.” 2013. Web. 26 Feb 2021.

Vancouver:

Potter CCJ. Kernel Selection for Convergence and Efficiency in Markov Chain Monte Carol. [Internet] [Thesis]. Carnegie Mellon University; 2013. [cited 2021 Feb 26]. Available from: http://repository.cmu.edu/dissertations/249.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Potter CCJ. Kernel Selection for Convergence and Efficiency in Markov Chain Monte Carol. [Thesis]. Carnegie Mellon University; 2013. Available from: http://repository.cmu.edu/dissertations/249

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of California – Berkeley

18. Kitchen, Nathan. Markov Chain Monte Carlo Stimulus Generation for Constrained Random Simulation.

Degree: Electrical Engineering & Computer Sciences, 2010, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/6gp3z1t0

► As integrated circuits have grown in size and complexity, the time required for functional verification has become the largest part of total design time. The… (more)

Subjects/Keywords: Electrical Engineering; Markov chain Monte Carlo; Metropolis algorithm; simulation; verification

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kitchen, N. (2010). Markov Chain Monte Carlo Stimulus Generation for Constrained Random Simulation. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/6gp3z1t0

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kitchen, Nathan. “Markov Chain Monte Carlo Stimulus Generation for Constrained Random Simulation.” 2010. Thesis, University of California – Berkeley. Accessed February 26, 2021. http://www.escholarship.org/uc/item/6gp3z1t0.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kitchen, Nathan. “Markov Chain Monte Carlo Stimulus Generation for Constrained Random Simulation.” 2010. Web. 26 Feb 2021.

Vancouver:

Kitchen N. Markov Chain Monte Carlo Stimulus Generation for Constrained Random Simulation. [Internet] [Thesis]. University of California – Berkeley; 2010. [cited 2021 Feb 26]. Available from: http://www.escholarship.org/uc/item/6gp3z1t0.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kitchen N. Markov Chain Monte Carlo Stimulus Generation for Constrained Random Simulation. [Thesis]. University of California – Berkeley; 2010. Available from: http://www.escholarship.org/uc/item/6gp3z1t0

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of California – Berkeley

19. Kominiarczuk, Jakub. Acyclic Monte Carlo: Efficient multi-level sampling of undirected graphical models through fast marginalization.

Degree: Mathematics, 2013, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/99d0g6x0

► We present a method for sampling high-dimensional probability spaces, applicable to Markov fields with both discrete and continuous variables, based on an approximate acyclic representation… (more)

▼ We present a method for sampling high-dimensional probability spaces, applicable to Markov fields with both discrete and continuous variables, based on an approximate acyclic representation of the probability density. Our method generalizes and places in a common framework some recent work on computing renormalized Hamiltonians and stochastic multigrid sampling.An acyclic representation of a probability distribution function (PDF) is obtained when one chooses an ordering of the variables and writes the PDF as a product of conditional probabilities, so that the probability of any variable is conditional only on the variables that precede it in the ordering. An acyclic representation makes the sampling efficient, because it uses the sparsity present in the model. We derive an approximate acyclic representation for general graphs by finding marginals through a fast marginalization scheme. The partial derivatives of the logarithm of the marginal probability are computed approximately through stochastic linear projection onto a polynomial basis, followed by reconstruction of the marginal through integration. The projection is based on an optimized inner product, making possible the use of Gaussian quadrature. Probability distributions involving discrete variables are handled by embedding the PDFs in differentiable extensions. Our algorithm can be extended to the evaluation of renormalized Hamiltonians formed using general renormalization schemes.The approximate acyclic representation of the PDF is then used for sampling. The variables are sampled in a fixed order, producing independent samples together with their sampling weights. We present an optimized sampling strategy that uses a maximum amount of information to choose individual variable values. The samples are further improved using techniques from particle filtering. We also introduce a block Markov chain Monte Carlo scheme based on the sampling weights. Finally, we present applications of our methodology to the Ising model.

Subjects/Keywords: Applied mathematics; Markov Chain Monte Carlo; Sampling; Simulation

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APA (6^th Edition):

Kominiarczuk, J. (2013). Acyclic Monte Carlo: Efficient multi-level sampling of undirected graphical models through fast marginalization. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/99d0g6x0

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kominiarczuk, Jakub. “Acyclic Monte Carlo: Efficient multi-level sampling of undirected graphical models through fast marginalization.” 2013. Thesis, University of California – Berkeley. Accessed February 26, 2021. http://www.escholarship.org/uc/item/99d0g6x0.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kominiarczuk, Jakub. “Acyclic Monte Carlo: Efficient multi-level sampling of undirected graphical models through fast marginalization.” 2013. Web. 26 Feb 2021.

Vancouver:

Kominiarczuk J. Acyclic Monte Carlo: Efficient multi-level sampling of undirected graphical models through fast marginalization. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2021 Feb 26]. Available from: http://www.escholarship.org/uc/item/99d0g6x0.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kominiarczuk J. Acyclic Monte Carlo: Efficient multi-level sampling of undirected graphical models through fast marginalization. [Thesis]. University of California – Berkeley; 2013. Available from: http://www.escholarship.org/uc/item/99d0g6x0

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

20. Fahrbach, Matthew. Probabilistic techniques for analyzing sampling algorithms and the dynamics of lattice models.

Degree: PhD, Computer Science, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/62274

► Statistical mechanics bridges the fields of physics and probability theory, providing critical insights into both disciplines. Statistical physics models capture key features of macroscopic phenomena… (more)

▼ Statistical mechanics bridges the fields of physics and probability theory, providing critical insights into both disciplines. Statistical physics models capture key features of macroscopic phenomena and consist of a set of configurations satisfying various constraints. Markov chain Monte Carlo algorithms are often used to sample from distributions over the exponentially large state space of these models to gain insight about the system and estimate its thermodynamic properties. Similar problems arise throughout machine learning, optimization, and counting complexity. In this dissertation, we present several new techniques based on random walks for analyzing sampling algorithms and the dynamics of various lattice models from statistical physics. We start by investigating the mixing time of Glauber dynamics for the six-vertex model in its ordered phases. We show that for every Boltzmann weight in the ferroelectric phase, there exist boundary conditions such that local Markov chains require exponential time to converge to equilibrium. This is the first rigorous result about the mixing time of Glauber dynamics for the six-vertex model in the ferroelectric phase. We also analyze the Glauber dynamics with free boundary conditions in the antiferroelectric phase and significantly extend the region for which local Markov chains are known to be slow mixing. In separate lines of work, we use techniques from the theory of random walks and electrical networks to give nearly tight bounds for the transience class of the Abelian sandpile model, closing an open problem of Babai and Gorodezky. The Abelian sandpile model is the canonical dynamical system used to study the phenomenon of self-organized criticality, and the transience class measures the time needed for the process to reach steady-state behavior. We also explore a new approach for approximately sampling elements with fixed rank from graded posets that relies solely on the mixing time of biased Markov chains. This allows us to bypass the usual obstacle of log-concavity. Last, we take a foray into analytic combinatorics and use the singularity analysis of Dirichlet generating functions to design sampling algorithms for Bose–Einstein condensates. Advisors/Committee Members: Randall, Dana (advisor), Peng, Richard (committee member), Singh, Mohit (committee member), Tetali, Prasad (committee member), Vigoda, Eric (committee member).

Subjects/Keywords: Lattice models; Markov chain Monte Carlo; Sampling algorithms; Statistical physics

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APA (6^th Edition):

Fahrbach, M. (2019). Probabilistic techniques for analyzing sampling algorithms and the dynamics of lattice models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62274

Chicago Manual of Style (16^th Edition):

Fahrbach, Matthew. “Probabilistic techniques for analyzing sampling algorithms and the dynamics of lattice models.” 2019. Doctoral Dissertation, Georgia Tech. Accessed February 26, 2021. http://hdl.handle.net/1853/62274.

MLA Handbook (7^th Edition):

Fahrbach, Matthew. “Probabilistic techniques for analyzing sampling algorithms and the dynamics of lattice models.” 2019. Web. 26 Feb 2021.

Vancouver:

Fahrbach M. Probabilistic techniques for analyzing sampling algorithms and the dynamics of lattice models. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1853/62274.

Council of Science Editors:

Fahrbach M. Probabilistic techniques for analyzing sampling algorithms and the dynamics of lattice models. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62274

University of Michigan

21. Adams, Caroline. Integrating Network and Intrinsic Changes in GnRH Neuron Control of Ovulation.

Degree: PhD, Mol & Integrtv Physiology PhD, 2020, University of Michigan

URL: http://hdl.handle.net/2027.42/155290

► Infertility affects 15-20% of couples; failure to ovulate is a common cause. Ovulation is triggered when estradiol switches from negative feedback action on the pituitary… (more)

▼ Infertility affects 15-20% of couples; failure to ovulate is a common cause. Ovulation is triggered when estradiol switches from negative feedback action on the pituitary and hypothalamus to positive feedback, initiating a surge of gonadotropin-releasing hormone (GnRH) secretion that causes a surge of luteinizing hormone (LH) release, which triggers ovulation. Our understanding of the neurobiological changes underlying the switch from negative to positive feedback is incomplete. High levels of estradiol are essential, and in rodents, the LH surge tends to occur at a specific time-of-day. GnRH neurons, however, do not express the estrogen receptor required for feedback, thus estradiol-sensitive afferents likely convey estradiol information to GnRH neurons. We hypothesized that GnRH neurons switch from negative to positive feedback by integrating multiple changes to their synaptic inputs and intrinsic properties. To investigate the neurobiological mechanisms that underlie surge generation, daily GnRH/LH surges can be induced by ovariectomy and estradiol replacement (OVX+E) in rodents. GnRH neuron activity and release are increased in the afternoon (positive feedback) and decreased in the morning (negative feedback). No time-of-day changes are observed in OVX mice that do not receive an estradiol implant. Previous studies using the daily surge model have elucidated multiple GnRH neuron intrinsic and fast-synaptic changes during the switch from negative to positive feedback. It is unclear which if any of these changes are necessary for increasing GnRH firing rate during positive feedback. We hypothesized that changes to GnRH neuron intrinsic properties culminate in an increase in excitability to current steps during positive feedback and a decrease in excitability during negative feedback. To our surprise, changes to GnRH neuron ionic conductances rendered GnRH neurons more excitable during positive feedback relative to all other groups, but changes to ionic conductances between OVX and negative feedback animals had no net effect on GnRH neuron excitability. A mathematical model using a novel application of a rigorous parameter estimation method predicted that multiple, redundant combinations of changes to GnRH intrinsic conductances can produce the firing response in positive feedback. Changes to two interdependent parameters that determine the kinetics of voltage-gated potassium channels accounted for the similar neural responses during negative feedback and in OVX mice. Although enhancing GnRH neuron excitability is expected to increase firing rate during positive feedback, it is unclear if this change is necessary or if the concomitant increase is fast-synaptic transmission is sufficient for increasing GnRH neural activity during positive feedback. To test this, we used dynamic clamp to inject positive feedback, negative feedback, and OVX postsynaptic conductance trains into cells from positive feedback, negative feedback, and OVX mice. Positive feedback conductance trains were more effective in initiating spiking in… Advisors/Committee Members: Moenter, Sue (committee member), Schnell, Santiago David (committee member), Booth, Victoria (committee member), Elias, Carol (committee member), Forger, Daniel Barclay (committee member), Murphy, Geoffrey G (committee member).

Subjects/Keywords: GnRH; kisspeptin; estradiol feedback; Markov-chain Monte Carlo method; Physiology; Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Adams, C. (2020). Integrating Network and Intrinsic Changes in GnRH Neuron Control of Ovulation. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/155290

Chicago Manual of Style (16^th Edition):

Adams, Caroline. “Integrating Network and Intrinsic Changes in GnRH Neuron Control of Ovulation.” 2020. Doctoral Dissertation, University of Michigan. Accessed February 26, 2021. http://hdl.handle.net/2027.42/155290.

MLA Handbook (7^th Edition):

Adams, Caroline. “Integrating Network and Intrinsic Changes in GnRH Neuron Control of Ovulation.” 2020. Web. 26 Feb 2021.

Vancouver:

Adams C. Integrating Network and Intrinsic Changes in GnRH Neuron Control of Ovulation. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/2027.42/155290.

Council of Science Editors:

Adams C. Integrating Network and Intrinsic Changes in GnRH Neuron Control of Ovulation. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/155290

Penn State University

22. Warner, Ashley Elizabeth. Statistical Skill in the Emulation of Climate Models.

Degree: 2014, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/22840

► A climate model of low complexity can be used to emulate the performance of one with higher complexity by identifying the parameters for each model… (more)

Subjects/Keywords: climate models; climate sensitivity; differential evolution; markov chain monte carlo

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APA (6^th Edition):

Warner, A. E. (2014). Statistical Skill in the Emulation of Climate Models. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22840

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Warner, Ashley Elizabeth. “Statistical Skill in the Emulation of Climate Models.” 2014. Thesis, Penn State University. Accessed February 26, 2021. https://submit-etda.libraries.psu.edu/catalog/22840.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Warner, Ashley Elizabeth. “Statistical Skill in the Emulation of Climate Models.” 2014. Web. 26 Feb 2021.

Vancouver:

Warner AE. Statistical Skill in the Emulation of Climate Models. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Feb 26]. Available from: https://submit-etda.libraries.psu.edu/catalog/22840.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Warner AE. Statistical Skill in the Emulation of Climate Models. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22840

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo

23. Saberi, Nastaran. Snow Properties Retrieval Using Passive Microwave Observations.

Degree: 2019, University of Waterloo

URL: http://hdl.handle.net/10012/14582

► Seasonal snow cover, the second-largest component of the cryosphere, is crucial in controlling the climate system, through its important role in modifying Earth’s albedo. The… (more)

▼ Seasonal snow cover, the second-largest component of the cryosphere, is crucial in controlling the climate system, through its important role in modifying Earth’s albedo. The temporal variability of snow extent and its physical properties in the seasonal cycle also make up a significant element to the cryospheric energy balance. Thus, seasonal snowcover should be monitored not only for its climatological impacts but also for its rolein the surface-water supply, ground-water recharge, and its insolation properties at local scales. Snowpack physical properties strongly influence the emissions from the substratum, making feasible snow property retrieval by means of the surface brightness temperature observed by passive microwave sensors. Depending on the observing spatial resolution, the time series records of daily snow coverage and a snowpacks most-critical properties such as the snow depth and snow water equivalent (SWE) could be helpful in applications ranging from modeling snow variations in a small catchment to global climatologic studies. However, the challenge of including spaceborne snow water equivalent (SWE) products in operational hydrological and hydroclimate modeling applications is very demanding with limited uptake by these systems. Various causes have been attributed to this lack of up-take but most stem from insufficient SWE accuracy. The root causes of this challenge includes the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process that are caused by uncertainties with the forward emission modeling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of the whole range of retrieval methodologies can provide the clarity needed to move the thinking forward in this important field. Following a review on snow depth and SWE retrieval methods using passive microwave remote sensing observations, this research employs a forward emission model to simulate snowpacks emission and compare the results to the PM airborne observations. Airborne radiometer observations coordinated with ground-based in-situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to the volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow…

Subjects/Keywords: Retrievals; SWE; Emission Model; Passive Microwave; Inversion; Monte Carlo Markov Chain

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APA (6^th Edition):

Saberi, N. (2019). Snow Properties Retrieval Using Passive Microwave Observations. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14582

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Saberi, Nastaran. “Snow Properties Retrieval Using Passive Microwave Observations.” 2019. Thesis, University of Waterloo. Accessed February 26, 2021. http://hdl.handle.net/10012/14582.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Saberi, Nastaran. “Snow Properties Retrieval Using Passive Microwave Observations.” 2019. Web. 26 Feb 2021.

Vancouver:

Saberi N. Snow Properties Retrieval Using Passive Microwave Observations. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10012/14582.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Saberi N. Snow Properties Retrieval Using Passive Microwave Observations. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14582

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Newcastle

24. Henriksen, Soren John. Computational Bayesian methods for communications and control.

Degree: PhD, 2013, University of Newcastle

URL: http://hdl.handle.net/1959.13/939998

►

Research Doctorate - Doctor of Philosophy (PhD)

As available computing power increases, there are now opportunities to use computational numerical methods to solve engineering problems… (more)

Subjects/Keywords: Bayesian; metropolis; system identification; Markov chain Monte Carlo; multi-user detection

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APA (6^th Edition):

Henriksen, S. J. (2013). Computational Bayesian methods for communications and control. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/939998

Chicago Manual of Style (16^th Edition):

Henriksen, Soren John. “Computational Bayesian methods for communications and control.” 2013. Doctoral Dissertation, University of Newcastle. Accessed February 26, 2021. http://hdl.handle.net/1959.13/939998.

MLA Handbook (7^th Edition):

Henriksen, Soren John. “Computational Bayesian methods for communications and control.” 2013. Web. 26 Feb 2021.

Vancouver:

Henriksen SJ. Computational Bayesian methods for communications and control. [Internet] [Doctoral dissertation]. University of Newcastle; 2013. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1959.13/939998.

Council of Science Editors:

Henriksen SJ. Computational Bayesian methods for communications and control. [Doctoral Dissertation]. University of Newcastle; 2013. Available from: http://hdl.handle.net/1959.13/939998

University of Guelph

25. Dobbs, Angie. Issues of Computational Efficiency and Model Approximation for Spatial Individual-Level Infectious Disease Models.

Degree: MS, Department of Mathematics and Statistics, 2012, University of Guelph

URL: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3248

► Individual-level models (ILMs) are models that can use the spatial-temporal nature of disease data to capture the disease dynamics. Parameter estimation is usually done via… (more)

Subjects/Keywords: epidemic models; Markov chain Monte Carlo; Bayesian inference; computational efficiency; normalization

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APA (6^th Edition):

Dobbs, A. (2012). Issues of Computational Efficiency and Model Approximation for Spatial Individual-Level Infectious Disease Models. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3248

Chicago Manual of Style (16^th Edition):

Dobbs, Angie. “Issues of Computational Efficiency and Model Approximation for Spatial Individual-Level Infectious Disease Models.” 2012. Masters Thesis, University of Guelph. Accessed February 26, 2021. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3248.

MLA Handbook (7^th Edition):

Dobbs, Angie. “Issues of Computational Efficiency and Model Approximation for Spatial Individual-Level Infectious Disease Models.” 2012. Web. 26 Feb 2021.

Vancouver:

Dobbs A. Issues of Computational Efficiency and Model Approximation for Spatial Individual-Level Infectious Disease Models. [Internet] [Masters thesis]. University of Guelph; 2012. [cited 2021 Feb 26]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3248.

Council of Science Editors:

Dobbs A. Issues of Computational Efficiency and Model Approximation for Spatial Individual-Level Infectious Disease Models. [Masters Thesis]. University of Guelph; 2012. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3248

Australian National University

26. Knapp, Simon Orlando. Land Use Mapping Using Constrained Monte Carlo Methods .

Degree: 2016, Australian National University

URL: http://hdl.handle.net/1885/133595

► We present a flexible, automated, Bayesian method designed for broad scale land use mapping. The method is based on a Monte Carlo Markov Chain and… (more)

Subjects/Keywords: Land Use; Markov Chain Monte Carlo; Remote Sensing

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APA (6^th Edition):

Knapp, S. O. (2016). Land Use Mapping Using Constrained Monte Carlo Methods . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/133595

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Knapp, Simon Orlando. “Land Use Mapping Using Constrained Monte Carlo Methods .” 2016. Thesis, Australian National University. Accessed February 26, 2021. http://hdl.handle.net/1885/133595.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Knapp, Simon Orlando. “Land Use Mapping Using Constrained Monte Carlo Methods .” 2016. Web. 26 Feb 2021.

Vancouver:

Knapp SO. Land Use Mapping Using Constrained Monte Carlo Methods . [Internet] [Thesis]. Australian National University; 2016. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1885/133595.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Knapp SO. Land Use Mapping Using Constrained Monte Carlo Methods . [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/133595

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Toronto

27. Otsubo, Kay Sarah. Comparing Apples to Apples: Measuring Mental Representations with Linked and Complete Markov Chain Monte Carlo.

Degree: 2019, University of Toronto

URL: http://hdl.handle.net/1807/98303

►

Previously, research exploring mental representations of categories has faced two challenges: 1) Preselection of stimuli by the experimenter limited full exploration of the mental representation.… (more)

Subjects/Keywords: Categorization; Experimental design; Markov chain Monte Carlo; Mental representation; 0633

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APA (6^th Edition):

Otsubo, K. S. (2019). Comparing Apples to Apples: Measuring Mental Representations with Linked and Complete Markov Chain Monte Carlo. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/98303

Chicago Manual of Style (16^th Edition):

Otsubo, Kay Sarah. “Comparing Apples to Apples: Measuring Mental Representations with Linked and Complete Markov Chain Monte Carlo.” 2019. Masters Thesis, University of Toronto. Accessed February 26, 2021. http://hdl.handle.net/1807/98303.

MLA Handbook (7^th Edition):

Otsubo, Kay Sarah. “Comparing Apples to Apples: Measuring Mental Representations with Linked and Complete Markov Chain Monte Carlo.” 2019. Web. 26 Feb 2021.

Vancouver:

Otsubo KS. Comparing Apples to Apples: Measuring Mental Representations with Linked and Complete Markov Chain Monte Carlo. [Internet] [Masters thesis]. University of Toronto; 2019. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1807/98303.

Council of Science Editors:

Otsubo KS. Comparing Apples to Apples: Measuring Mental Representations with Linked and Complete Markov Chain Monte Carlo. [Masters Thesis]. University of Toronto; 2019. Available from: http://hdl.handle.net/1807/98303

University of Gothenburg / Göteborgs Universitet

28. Hammar, Oscar. Percolation: Inference and Applications in Hydrology.

Degree: 2011, University of Gothenburg / Göteborgs Universitet

URL: http://hdl.handle.net/2077/27883

► Percolation theory is a branch of probability theory describing connectedness in a stochastic network. The connectedness of a percolation process is governed by a few,… (more)

Subjects/Keywords: percolation; inference; consistency; Markov chain Monte Carlo; hydrology

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APA (6^th Edition):

Hammar, O. (2011). Percolation: Inference and Applications in Hydrology. (Thesis). University of Gothenburg / Göteborgs Universitet. Retrieved from http://hdl.handle.net/2077/27883

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hammar, Oscar. “Percolation: Inference and Applications in Hydrology.” 2011. Thesis, University of Gothenburg / Göteborgs Universitet. Accessed February 26, 2021. http://hdl.handle.net/2077/27883.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hammar, Oscar. “Percolation: Inference and Applications in Hydrology.” 2011. Web. 26 Feb 2021.

Vancouver:

Hammar O. Percolation: Inference and Applications in Hydrology. [Internet] [Thesis]. University of Gothenburg / Göteborgs Universitet; 2011. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/2077/27883.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hammar O. Percolation: Inference and Applications in Hydrology. [Thesis]. University of Gothenburg / Göteborgs Universitet; 2011. Available from: http://hdl.handle.net/2077/27883

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rice University

29. Vankov, Emilian. Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods.

Degree: PhD, Engineering, 2015, Rice University

URL: http://hdl.handle.net/1911/107995

► A new approach to state filtering and parameter estimation for a class of stochastic volatility models for which the likelihood function is unknown is considered.… (more)

▼ A new approach to state filtering and parameter estimation for a class of stochastic volatility models for which the likelihood function is unknown is considered. The alpha-stable stochastic volatility model provides a flexible framework for modeling asymmetry and heavy tails, which is useful when modeling financial returns. However, a problem posed by the alpha-stable distribution is the lack of a closed form for the probability density function, which prevents its direct application to standard filtering and estimation techniques such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). To circumvent this difficulty, researchers have recently developed various approximate Bayesian computation (ABC) methods, which require only that one is able to simulate data from the model. To obtain filtered volatility estimates, we develop a novel ABC based auxiliary particle filter (APF-ABC). The algorithm we develop can be easily applied to many state space models for which the likelihood function is intractable or computationally expensive. APF-ABC improves on the accuracy through better proposal distributions in cases where the optimal importance density of the filter is unavailable. Further, a new particle based MCMC (PMCMC) method is proposed for parameter estimation in this class of volatility models. PMCMC methods combine SMC with MCMC to produce samples from the joint stationary distribution of the latent states and parameters. If full conditional distributions for all parameters are available then the particle Gibbs sampler is typically adopted; otherwise, the particle marginal Metropolis-Hastings can be used for posterior estimation. Although, several ABC based extensions of PMCMC have been proposed for the symmetric alpha-stable stochastic volatility model, all have used the particle marginal Metropolis-Hastings algorithm due to the inability to obtain full conditional distributions for all parameters in the model. However, the availability of full conditional distributions for a subset of the parameters raises the natural question of whether it is possible to estimate some of the parameters using their full conditionals, while others using a Metropolis-Hastings step. The algorithm that is proposed addresses this exact question. It is shown through a simulation study, that such a strategy can lead to increases in efficiency in the estimation process. Moreover, in contrast to previous works, this thesis studies the asymmetric alpha-stable stochastic volatility model. Advisors/Committee Members: Ensor, Katherine B (advisor).

Subjects/Keywords: Stochastic Volatility; Stable Distribution; Approximate Bayesian Computation; Markov Chain Monte Carlo

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APA (6^th Edition):

Vankov, E. (2015). Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/107995

Chicago Manual of Style (16^th Edition):

Vankov, Emilian. “Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods.” 2015. Doctoral Dissertation, Rice University. Accessed February 26, 2021. http://hdl.handle.net/1911/107995.

MLA Handbook (7^th Edition):

Vankov, Emilian. “Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods.” 2015. Web. 26 Feb 2021.

Vancouver:

Vankov E. Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods. [Internet] [Doctoral dissertation]. Rice University; 2015. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1911/107995.

Council of Science Editors:

Vankov E. Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods. [Doctoral Dissertation]. Rice University; 2015. Available from: http://hdl.handle.net/1911/107995

Delft University of Technology

30. Butler, Jacob (author). Bayesian Identification of Thermodynamic Parameters from Shock Tube Data.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:6c0a3871-aaca-45d3-80dc-a4265f95088a

► The project concerns uncertainty reduction of parameters of a thermodynamic equation of state for a dense gas, using Bayesian inference. The dense gas considered is… (more)

Subjects/Keywords: Bayesian Inference; Dense gas flow; Markov Chain Monte Carlo

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Butler, J. (. (2018). Bayesian Identification of Thermodynamic Parameters from Shock Tube Data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6c0a3871-aaca-45d3-80dc-a4265f95088a

Chicago Manual of Style (16^th Edition):

Butler, Jacob (author). “Bayesian Identification of Thermodynamic Parameters from Shock Tube Data.” 2018. Masters Thesis, Delft University of Technology. Accessed February 26, 2021. http://resolver.tudelft.nl/uuid:6c0a3871-aaca-45d3-80dc-a4265f95088a.

MLA Handbook (7^th Edition):

Butler, Jacob (author). “Bayesian Identification of Thermodynamic Parameters from Shock Tube Data.” 2018. Web. 26 Feb 2021.

Vancouver:

Butler J(. Bayesian Identification of Thermodynamic Parameters from Shock Tube Data. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:6c0a3871-aaca-45d3-80dc-a4265f95088a.

Council of Science Editors:

Butler J(. Bayesian Identification of Thermodynamic Parameters from Shock Tube Data. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:6c0a3871-aaca-45d3-80dc-a4265f95088a

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