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You searched for subject:(Fractional Stochastic Differential Equation Bayesian inference Markov chain Monte Carlo Sequential Monte carlo Methods Particle Filter Auxiliary Particle Filter ). Showing records 1 – 30 of 64202 total matches.

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University of Plymouth

1. Al-Saadony, Muhannad. Bayesian stochastic differential equation modelling with application to finance.

Degree: PhD, 2013, University of Plymouth

 In this thesis, we consider some popular stochastic differential equation models used in finance, such as the Vasicek Interest Rate model, the Heston model and… (more)

Subjects/Keywords: 519.2; Fractional Stochastic Differential Equation, Bayesian inference, Markov chain Monte Carlo, Sequential Monte carlo Methods, Particle Filter, Auxiliary Particle Filter.

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

APA (6th Edition):

Al-Saadony, M. (2013). Bayesian stochastic differential equation modelling with application to finance. (Doctoral Dissertation). University of Plymouth. Retrieved from http://hdl.handle.net/10026.1/1530

Chicago Manual of Style (16th Edition):

Al-Saadony, Muhannad. “Bayesian stochastic differential equation modelling with application to finance.” 2013. Doctoral Dissertation, University of Plymouth. Accessed July 23, 2019. http://hdl.handle.net/10026.1/1530.

MLA Handbook (7th Edition):

Al-Saadony, Muhannad. “Bayesian stochastic differential equation modelling with application to finance.” 2013. Web. 23 Jul 2019.

Vancouver:

Al-Saadony M. Bayesian stochastic differential equation modelling with application to finance. [Internet] [Doctoral dissertation]. University of Plymouth; 2013. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10026.1/1530.

Council of Science Editors:

Al-Saadony M. Bayesian stochastic differential equation modelling with application to finance. [Doctoral Dissertation]. University of Plymouth; 2013. Available from: http://hdl.handle.net/10026.1/1530


University of Lund

2. Nordh, Jerker. Bayesian Inference for Nonlinear Dynamical Systems : Applications and Software Implementation.

Degree: 2015, University of Lund

 The topic of this thesis is estimation of nonlinear dynamical systems, focusing on the use of methods such as particle filtering and smoothing. There are… (more)

Subjects/Keywords: Reglerteknik; Indoor Navigation; pyParticleEst; Software Implementation; Simultaneous Localization and Mapping; Parameter Estimation; System Identification; Sequential Importance Sampling; Particle Filter; Bayesian Inference; Markov Chain Monte Carlo; Particle Smoother

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APA (6th Edition):

Nordh, J. (2015). Bayesian Inference for Nonlinear Dynamical Systems : Applications and Software Implementation. (Doctoral Dissertation). University of Lund. Retrieved from http://lup.lub.lu.se/record/5423572 ; http://portal.research.lu.se/ws/files/4309909/5423599.pdf

Chicago Manual of Style (16th Edition):

Nordh, Jerker. “Bayesian Inference for Nonlinear Dynamical Systems : Applications and Software Implementation.” 2015. Doctoral Dissertation, University of Lund. Accessed July 23, 2019. http://lup.lub.lu.se/record/5423572 ; http://portal.research.lu.se/ws/files/4309909/5423599.pdf.

MLA Handbook (7th Edition):

Nordh, Jerker. “Bayesian Inference for Nonlinear Dynamical Systems : Applications and Software Implementation.” 2015. Web. 23 Jul 2019.

Vancouver:

Nordh J. Bayesian Inference for Nonlinear Dynamical Systems : Applications and Software Implementation. [Internet] [Doctoral dissertation]. University of Lund; 2015. [cited 2019 Jul 23]. Available from: http://lup.lub.lu.se/record/5423572 ; http://portal.research.lu.se/ws/files/4309909/5423599.pdf.

Council of Science Editors:

Nordh J. Bayesian Inference for Nonlinear Dynamical Systems : Applications and Software Implementation. [Doctoral Dissertation]. University of Lund; 2015. Available from: http://lup.lub.lu.se/record/5423572 ; http://portal.research.lu.se/ws/files/4309909/5423599.pdf


University of Melbourne

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

Degree: 2011, University of Melbourne

 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)

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 (6th 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 (16th 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 July 23, 2019. http://hdl.handle.net/11343/36875.

MLA Handbook (7th Edition):

Vu, Tuyet Thi Anh. “A Particle Markov Chain Monte Carlo algorithm for random finite set based multi-target tracking.” 2011. Web. 23 Jul 2019.

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 2019 Jul 23]. 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


Georgia State University

4. Wu, Peisheng. SEQUENTIAL MONTE CARLO BASED DATA ASSIMILATION FRAMEWORK AND TOOLKIT FOR DYNAMIC SYSTEM SIMULATIONS.

Degree: PhD, Computer Science, 2017, Georgia State University

  Assimilating real-time sensor data into simulations is an effective approach for improving predictive abilities. However, integrating complex simulation models, e.g., discrete event simulation models… (more)

Subjects/Keywords: Data Assimilation; Sequential Monte Carlo methods; particle filter; simulation; general framework; open source

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APA (6th Edition):

Wu, P. (2017). SEQUENTIAL MONTE CARLO BASED DATA ASSIMILATION FRAMEWORK AND TOOLKIT FOR DYNAMIC SYSTEM SIMULATIONS. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_diss/131

Chicago Manual of Style (16th Edition):

Wu, Peisheng. “SEQUENTIAL MONTE CARLO BASED DATA ASSIMILATION FRAMEWORK AND TOOLKIT FOR DYNAMIC SYSTEM SIMULATIONS.” 2017. Doctoral Dissertation, Georgia State University. Accessed July 23, 2019. https://scholarworks.gsu.edu/cs_diss/131.

MLA Handbook (7th Edition):

Wu, Peisheng. “SEQUENTIAL MONTE CARLO BASED DATA ASSIMILATION FRAMEWORK AND TOOLKIT FOR DYNAMIC SYSTEM SIMULATIONS.” 2017. Web. 23 Jul 2019.

Vancouver:

Wu P. SEQUENTIAL MONTE CARLO BASED DATA ASSIMILATION FRAMEWORK AND TOOLKIT FOR DYNAMIC SYSTEM SIMULATIONS. [Internet] [Doctoral dissertation]. Georgia State University; 2017. [cited 2019 Jul 23]. Available from: https://scholarworks.gsu.edu/cs_diss/131.

Council of Science Editors:

Wu P. SEQUENTIAL MONTE CARLO BASED DATA ASSIMILATION FRAMEWORK AND TOOLKIT FOR DYNAMIC SYSTEM SIMULATIONS. [Doctoral Dissertation]. Georgia State University; 2017. Available from: https://scholarworks.gsu.edu/cs_diss/131


Penn State University

5. Tibbits, Matthew McLean. Parallel Multivariate Slice Sampling.

Degree: MS, Statistics, 2009, Penn State University

 Slice sampling provides an easily implemented method for constructing a Markov chain Monte Carlo (MCMC) algorithm. However, slice sampling has two major drawbacks: (i) it… (more)

Subjects/Keywords: Bayesian inference; Markov chain Monte Carlo; Para

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APA (6th Edition):

Tibbits, M. M. (2009). Parallel Multivariate Slice Sampling. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/9151

Chicago Manual of Style (16th Edition):

Tibbits, Matthew McLean. “Parallel Multivariate Slice Sampling.” 2009. Masters Thesis, Penn State University. Accessed July 23, 2019. https://etda.libraries.psu.edu/catalog/9151.

MLA Handbook (7th Edition):

Tibbits, Matthew McLean. “Parallel Multivariate Slice Sampling.” 2009. Web. 23 Jul 2019.

Vancouver:

Tibbits MM. Parallel Multivariate Slice Sampling. [Internet] [Masters thesis]. Penn State University; 2009. [cited 2019 Jul 23]. Available from: https://etda.libraries.psu.edu/catalog/9151.

Council of Science Editors:

Tibbits MM. Parallel Multivariate Slice Sampling. [Masters Thesis]. Penn State University; 2009. Available from: https://etda.libraries.psu.edu/catalog/9151


University of New South Wales

6. Peters, Gareth William. Advances in approximate Bayesian computation and trans-dimensional sampling methodology.

Degree: Mathematics & Statistics, 2010, University of New South Wales

Bayesian statistical models continue to grow in complexity, drivenin part by a few key factors: the massive computational resourcesnow available to statisticians; the substantial gains… (more)

Subjects/Keywords: Sequential Monte Carlo; Markov Chain Monte Carlo; Approximate Bayesian Computation

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

APA (6th Edition):

Peters, G. W. (2010). Advances in approximate Bayesian computation and trans-dimensional sampling methodology. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/50086 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:9018/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Peters, Gareth William. “Advances in approximate Bayesian computation and trans-dimensional sampling methodology.” 2010. Doctoral Dissertation, University of New South Wales. Accessed July 23, 2019. http://handle.unsw.edu.au/1959.4/50086 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:9018/SOURCE02?view=true.

MLA Handbook (7th Edition):

Peters, Gareth William. “Advances in approximate Bayesian computation and trans-dimensional sampling methodology.” 2010. Web. 23 Jul 2019.

Vancouver:

Peters GW. Advances in approximate Bayesian computation and trans-dimensional sampling methodology. [Internet] [Doctoral dissertation]. University of New South Wales; 2010. [cited 2019 Jul 23]. Available from: http://handle.unsw.edu.au/1959.4/50086 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:9018/SOURCE02?view=true.

Council of Science Editors:

Peters GW. Advances in approximate Bayesian computation and trans-dimensional sampling methodology. [Doctoral Dissertation]. University of New South Wales; 2010. Available from: http://handle.unsw.edu.au/1959.4/50086 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:9018/SOURCE02?view=true


University of Oxford

7. Allen, K. E. Segmentation and analysis of vascular networks.

Degree: PhD, 2010, University of Oxford

 From a clinical perspective retinal vascular segmentation and analysis are important tasks in aiding quantification of vascular disease progression for such prevalent pathologies as diabetic… (more)

Subjects/Keywords: 617.7; Applications and algorithms; Biomedical engineering; Vascular research; Stochastic processes; vascular segmentation; image segmentation; vascular analysis; particle filter; Markov Chain Monte Carlo

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APA (6th Edition):

Allen, K. E. (2010). Segmentation and analysis of vascular networks. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:1674441f-aa9a-468e-a589-8e0f1ef6b417 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547445

Chicago Manual of Style (16th Edition):

Allen, K E. “Segmentation and analysis of vascular networks.” 2010. Doctoral Dissertation, University of Oxford. Accessed July 23, 2019. http://ora.ox.ac.uk/objects/uuid:1674441f-aa9a-468e-a589-8e0f1ef6b417 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547445.

MLA Handbook (7th Edition):

Allen, K E. “Segmentation and analysis of vascular networks.” 2010. Web. 23 Jul 2019.

Vancouver:

Allen KE. Segmentation and analysis of vascular networks. [Internet] [Doctoral dissertation]. University of Oxford; 2010. [cited 2019 Jul 23]. Available from: http://ora.ox.ac.uk/objects/uuid:1674441f-aa9a-468e-a589-8e0f1ef6b417 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547445.

Council of Science Editors:

Allen KE. Segmentation and analysis of vascular networks. [Doctoral Dissertation]. University of Oxford; 2010. Available from: http://ora.ox.ac.uk/objects/uuid:1674441f-aa9a-468e-a589-8e0f1ef6b417 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547445


University of Texas – Austin

8. Sun, Wenwen. Sampling approaches in Bayesian computational statistics with R.

Degree: Mathematics, 2009, University of Texas – Austin

Bayesian analysis is definitely different from the classic statistical methods. Although, both of them use subjective ideas, it is used in the selection of models… (more)

Subjects/Keywords: Bayesian; Markov chain Monte Carlo

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APA (6th Edition):

Sun, W. (2009). Sampling approaches in Bayesian computational statistics with R. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2009-12-611

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

Chicago Manual of Style (16th Edition):

Sun, Wenwen. “Sampling approaches in Bayesian computational statistics with R.” 2009. Thesis, University of Texas – Austin. Accessed July 23, 2019. http://hdl.handle.net/2152/ETD-UT-2009-12-611.

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

MLA Handbook (7th Edition):

Sun, Wenwen. “Sampling approaches in Bayesian computational statistics with R.” 2009. Web. 23 Jul 2019.

Vancouver:

Sun W. Sampling approaches in Bayesian computational statistics with R. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/2152/ETD-UT-2009-12-611.

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

Council of Science Editors:

Sun W. Sampling approaches in Bayesian computational statistics with R. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/ETD-UT-2009-12-611

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


Texas A&M University

9. Boddikurapati, Sirish. Sequential Monte Carlo Methods With Applications To Communication Channels.

Degree: 2010, Texas A&M University

 Estimating the state of a system from noisy measurements is a problem which arises in a variety of scientific and industrial areas which include signal… (more)

Subjects/Keywords: Particle filtering; Sequential Monte Carlo filtering; Markovian chains; Recursive Bayesian filtering; Continuous-Discrete particle filter; optical fiber propagation; capacity of optical fiber; information rate using particle filtering

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APA (6th Edition):

Boddikurapati, S. (2010). Sequential Monte Carlo Methods With Applications To Communication Channels. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537

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

Chicago Manual of Style (16th Edition):

Boddikurapati, Sirish. “Sequential Monte Carlo Methods With Applications To Communication Channels.” 2010. Thesis, Texas A&M University. Accessed July 23, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537.

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

MLA Handbook (7th Edition):

Boddikurapati, Sirish. “Sequential Monte Carlo Methods With Applications To Communication Channels.” 2010. Web. 23 Jul 2019.

Vancouver:

Boddikurapati S. Sequential Monte Carlo Methods With Applications To Communication Channels. [Internet] [Thesis]. Texas A&M University; 2010. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537.

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

Council of Science Editors:

Boddikurapati S. Sequential Monte Carlo Methods With Applications To Communication Channels. [Thesis]. Texas A&M University; 2010. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537

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


University of Texas – Austin

10. Bond, Mark Arjun. Using the filter-forward backward sampling algorithm in second-order Bayesian latent growth modeling.

Degree: Statistics, 2016, University of Texas – Austin

 In educational and social science research, large-scale testing data are frequently collected longitudinally so that researchers can evaluate change over time. Researchers may then wish… (more)

Subjects/Keywords: MCMC; Markov chain Monte Carlo; Time series; Autoregression; Bayesian estimation; Latent growth modeling; Kalman filter

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

APA (6th Edition):

Bond, M. A. (2016). Using the filter-forward backward sampling algorithm in second-order Bayesian latent growth modeling. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47132

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

Chicago Manual of Style (16th Edition):

Bond, Mark Arjun. “Using the filter-forward backward sampling algorithm in second-order Bayesian latent growth modeling.” 2016. Thesis, University of Texas – Austin. Accessed July 23, 2019. http://hdl.handle.net/2152/47132.

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

MLA Handbook (7th Edition):

Bond, Mark Arjun. “Using the filter-forward backward sampling algorithm in second-order Bayesian latent growth modeling.” 2016. Web. 23 Jul 2019.

Vancouver:

Bond MA. Using the filter-forward backward sampling algorithm in second-order Bayesian latent growth modeling. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/2152/47132.

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

Council of Science Editors:

Bond MA. Using the filter-forward backward sampling algorithm in second-order Bayesian latent growth modeling. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/47132

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


University of California – Irvine

11. Zhang, Cheng. Scalable Hamiltonian Monte Carlo via Surrogate Methods.

Degree: Mathematics, 2016, University of California – Irvine

Markov chain Monte Carlo (MCMC) methods have been widely used in Bayesian inference involving intractable probabilistic models. However, simple MCMC algorithms (e.g., random walk Metropolis… (more)

Subjects/Keywords: Applied mathematics; Statistics; Markov chain Monte Carlo; Random Network; Scalable Bayesian inference; Surrogate Methods

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APA (6th Edition):

Zhang, C. (2016). Scalable Hamiltonian Monte Carlo via Surrogate Methods. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/61j792r1

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

Chicago Manual of Style (16th Edition):

Zhang, Cheng. “Scalable Hamiltonian Monte Carlo via Surrogate Methods.” 2016. Thesis, University of California – Irvine. Accessed July 23, 2019. http://www.escholarship.org/uc/item/61j792r1.

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

MLA Handbook (7th Edition):

Zhang, Cheng. “Scalable Hamiltonian Monte Carlo via Surrogate Methods.” 2016. Web. 23 Jul 2019.

Vancouver:

Zhang C. Scalable Hamiltonian Monte Carlo via Surrogate Methods. [Internet] [Thesis]. University of California – Irvine; 2016. [cited 2019 Jul 23]. Available from: http://www.escholarship.org/uc/item/61j792r1.

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

Council of Science Editors:

Zhang C. Scalable Hamiltonian Monte Carlo via Surrogate Methods. [Thesis]. University of California – Irvine; 2016. Available from: http://www.escholarship.org/uc/item/61j792r1

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


Australian National University

12. Lim, Kar Wai. Bayesian analysis of claim run-off triangles .

Degree: 2011, Australian National University

 This dissertation studies Markov chain Monte Carlo (MCMC) methods, and applies them to actuarial data, with a focus on claim run-off triangles. After reviewing a… (more)

Subjects/Keywords: Bayesian inference; Markov chain Monte Carlo (MCMC) methods; claim run-off triangles

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

APA (6th Edition):

Lim, K. W. (2011). Bayesian analysis of claim run-off triangles . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/106530

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

Chicago Manual of Style (16th Edition):

Lim, Kar Wai. “Bayesian analysis of claim run-off triangles .” 2011. Thesis, Australian National University. Accessed July 23, 2019. http://hdl.handle.net/1885/106530.

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

MLA Handbook (7th Edition):

Lim, Kar Wai. “Bayesian analysis of claim run-off triangles .” 2011. Web. 23 Jul 2019.

Vancouver:

Lim KW. Bayesian analysis of claim run-off triangles . [Internet] [Thesis]. Australian National University; 2011. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/1885/106530.

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

Council of Science Editors:

Lim KW. Bayesian analysis of claim run-off triangles . [Thesis]. Australian National University; 2011. Available from: http://hdl.handle.net/1885/106530

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


Loughborough University

13. Daniyan, Abdullahi. Advanced signal processing techniques for multi-target tracking.

Degree: PhD, 2018, Loughborough University

 The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene,… (more)

Subjects/Keywords: Target tracking; Bayesian inference; Bayesian estimation; Signal Processing; Kalman filter; Random finite sets; RFS; Particle filter; PHD filter; Probability hypothesis filter; CPHD filter; Sequential Monte Carlo; Game theory; Regret matching; Correlated equilibrium; Passive radar; Statistical signal processing; Probability; GLMB; Labelled random finite sets; Kalman gain; Poisson mixture; B-spline

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APA (6th Edition):

Daniyan, A. (2018). Advanced signal processing techniques for multi-target tracking. (Doctoral Dissertation). Loughborough University. Retrieved from https://dspace.lboro.ac.uk/2134/35277 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763466

Chicago Manual of Style (16th Edition):

Daniyan, Abdullahi. “Advanced signal processing techniques for multi-target tracking.” 2018. Doctoral Dissertation, Loughborough University. Accessed July 23, 2019. https://dspace.lboro.ac.uk/2134/35277 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763466.

MLA Handbook (7th Edition):

Daniyan, Abdullahi. “Advanced signal processing techniques for multi-target tracking.” 2018. Web. 23 Jul 2019.

Vancouver:

Daniyan A. Advanced signal processing techniques for multi-target tracking. [Internet] [Doctoral dissertation]. Loughborough University; 2018. [cited 2019 Jul 23]. Available from: https://dspace.lboro.ac.uk/2134/35277 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763466.

Council of Science Editors:

Daniyan A. Advanced signal processing techniques for multi-target tracking. [Doctoral Dissertation]. Loughborough University; 2018. Available from: https://dspace.lboro.ac.uk/2134/35277 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763466

14. Dubarry, Cyrille. Méthodes de lissage et d'estimation dans des modèles à variables latentes par des méthodes de Monte-Carlo séquentielles : Smoothing and estimation methods in hidden variable models through sequential Monte-Carlo methods.

Degree: Docteur es, Mathématiques, 2012, Evry, Institut national des télécommunications

Les modèles de chaînes de Markov cachées ou plus généralement ceux de Feynman-Kac sont aujourd'hui très largement utilisés. Ils permettent de modéliser une grande diversité… (more)

Subjects/Keywords: Monte-Carlo séquentiel; Filtrage particulaire; Lissage particulaire; Feynman-Kac; Chaîne de Markov caché; Sequential Monte-Carlo; Particle filtering; Particle smoothing; Hidden markov chain

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APA (6th Edition):

Dubarry, C. (2012). Méthodes de lissage et d'estimation dans des modèles à variables latentes par des méthodes de Monte-Carlo séquentielles : Smoothing and estimation methods in hidden variable models through sequential Monte-Carlo methods. (Doctoral Dissertation). Evry, Institut national des télécommunications. Retrieved from http://www.theses.fr/2012TELE0040

Chicago Manual of Style (16th Edition):

Dubarry, Cyrille. “Méthodes de lissage et d'estimation dans des modèles à variables latentes par des méthodes de Monte-Carlo séquentielles : Smoothing and estimation methods in hidden variable models through sequential Monte-Carlo methods.” 2012. Doctoral Dissertation, Evry, Institut national des télécommunications. Accessed July 23, 2019. http://www.theses.fr/2012TELE0040.

MLA Handbook (7th Edition):

Dubarry, Cyrille. “Méthodes de lissage et d'estimation dans des modèles à variables latentes par des méthodes de Monte-Carlo séquentielles : Smoothing and estimation methods in hidden variable models through sequential Monte-Carlo methods.” 2012. Web. 23 Jul 2019.

Vancouver:

Dubarry C. Méthodes de lissage et d'estimation dans des modèles à variables latentes par des méthodes de Monte-Carlo séquentielles : Smoothing and estimation methods in hidden variable models through sequential Monte-Carlo methods. [Internet] [Doctoral dissertation]. Evry, Institut national des télécommunications; 2012. [cited 2019 Jul 23]. Available from: http://www.theses.fr/2012TELE0040.

Council of Science Editors:

Dubarry C. Méthodes de lissage et d'estimation dans des modèles à variables latentes par des méthodes de Monte-Carlo séquentielles : Smoothing and estimation methods in hidden variable models through sequential Monte-Carlo methods. [Doctoral Dissertation]. Evry, Institut national des télécommunications; 2012. Available from: http://www.theses.fr/2012TELE0040


University of Wollongong

15. Walgama Wellalage, Niroshan Karunarathna. Predicting remaining service potential of railway bridges based on visual inspection data.

Degree: PhD, 2015, University of Wollongong

  Asset authorities need to predict the future condition of infrastructure, including railway bridges as part of the process of managing asset integrity and to… (more)

Subjects/Keywords: stochastic deterioration models; optimization; Bayesian inference; Markov Chain Monte Carlo simulation; Markov models; non-homogenous Markov

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APA (6th Edition):

Walgama Wellalage, N. K. (2015). Predicting remaining service potential of railway bridges based on visual inspection data. (Doctoral Dissertation). University of Wollongong. Retrieved from 010206 Operations Research, 010303 Optimisation, 080201 Analysis of Algorithms and Complexity, 090505 Infrastructure Engineering and Asset Management ; https://ro.uow.edu.au/theses/4610

Chicago Manual of Style (16th Edition):

Walgama Wellalage, Niroshan Karunarathna. “Predicting remaining service potential of railway bridges based on visual inspection data.” 2015. Doctoral Dissertation, University of Wollongong. Accessed July 23, 2019. 010206 Operations Research, 010303 Optimisation, 080201 Analysis of Algorithms and Complexity, 090505 Infrastructure Engineering and Asset Management ; https://ro.uow.edu.au/theses/4610.

MLA Handbook (7th Edition):

Walgama Wellalage, Niroshan Karunarathna. “Predicting remaining service potential of railway bridges based on visual inspection data.” 2015. Web. 23 Jul 2019.

Vancouver:

Walgama Wellalage NK. Predicting remaining service potential of railway bridges based on visual inspection data. [Internet] [Doctoral dissertation]. University of Wollongong; 2015. [cited 2019 Jul 23]. Available from: 010206 Operations Research, 010303 Optimisation, 080201 Analysis of Algorithms and Complexity, 090505 Infrastructure Engineering and Asset Management ; https://ro.uow.edu.au/theses/4610.

Council of Science Editors:

Walgama Wellalage NK. Predicting remaining service potential of railway bridges based on visual inspection data. [Doctoral Dissertation]. University of Wollongong; 2015. Available from: 010206 Operations Research, 010303 Optimisation, 080201 Analysis of Algorithms and Complexity, 090505 Infrastructure Engineering and Asset Management ; https://ro.uow.edu.au/theses/4610


University of Arizona

16. Woodard, Aaron Jacob. Bayesian Estimation of a Single Mass Concentration Within an Asteroid .

Degree: 2017, University of Arizona

 Orbit determination has long relied on the use of the Kalman filter, or specifically the extended Kalman filter, as a means of accurately navigating spacecraft.… (more)

Subjects/Keywords: Asteroid; Bayesian Estimation; Gravity Field; Sequential Monte Carlo Filter

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APA (6th Edition):

Woodard, A. J. (2017). Bayesian Estimation of a Single Mass Concentration Within an Asteroid . (Masters Thesis). University of Arizona. Retrieved from http://hdl.handle.net/10150/625702

Chicago Manual of Style (16th Edition):

Woodard, Aaron Jacob. “Bayesian Estimation of a Single Mass Concentration Within an Asteroid .” 2017. Masters Thesis, University of Arizona. Accessed July 23, 2019. http://hdl.handle.net/10150/625702.

MLA Handbook (7th Edition):

Woodard, Aaron Jacob. “Bayesian Estimation of a Single Mass Concentration Within an Asteroid .” 2017. Web. 23 Jul 2019.

Vancouver:

Woodard AJ. Bayesian Estimation of a Single Mass Concentration Within an Asteroid . [Internet] [Masters thesis]. University of Arizona; 2017. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10150/625702.

Council of Science Editors:

Woodard AJ. Bayesian Estimation of a Single Mass Concentration Within an Asteroid . [Masters Thesis]. University of Arizona; 2017. Available from: http://hdl.handle.net/10150/625702


Dalhousie University

17. Wong, Jessica. Parameter Estimation for Nonlinear State Space Models.

Degree: MS, Department of Mathematics & Statistics - Statistics Division, 2012, Dalhousie University

Master's thesis

This thesis explores the methodology of state, and in particular, parameter estimation for time series datasets. Various approaches are investigated that are suitable… (more)

Subjects/Keywords: Lotka Volterra; Predator Prey; state space models; particle filter; particle Markov chain Monte Carlo; maximum likelihood estimation; state augmentation; multiple iterative filtering

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APA (6th Edition):

Wong, J. (2012). Parameter Estimation for Nonlinear State Space Models. (Masters Thesis). Dalhousie University. Retrieved from http://hdl.handle.net/10222/14741

Chicago Manual of Style (16th Edition):

Wong, Jessica. “Parameter Estimation for Nonlinear State Space Models.” 2012. Masters Thesis, Dalhousie University. Accessed July 23, 2019. http://hdl.handle.net/10222/14741.

MLA Handbook (7th Edition):

Wong, Jessica. “Parameter Estimation for Nonlinear State Space Models.” 2012. Web. 23 Jul 2019.

Vancouver:

Wong J. Parameter Estimation for Nonlinear State Space Models. [Internet] [Masters thesis]. Dalhousie University; 2012. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10222/14741.

Council of Science Editors:

Wong J. Parameter Estimation for Nonlinear State Space Models. [Masters Thesis]. Dalhousie University; 2012. Available from: http://hdl.handle.net/10222/14741


University of Waterloo

18. Datta Gupta, Syamantak. A Comparative Study of the Particle Filter and the Ensemble Kalman Filter.

Degree: 2009, University of Waterloo

 Non-linear Bayesian estimation, or estimation of the state of a non-linear stochastic system from a set of indirect noisy measurements is a problem encountered in… (more)

Subjects/Keywords: Bayesian estimation; non-linear filtering; particle filter; ensemble Kalman filter; Monte Carlo methods; Bayesian inference

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APA (6th Edition):

Datta Gupta, S. (2009). A Comparative Study of the Particle Filter and the Ensemble Kalman Filter. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/4503

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

Chicago Manual of Style (16th Edition):

Datta Gupta, Syamantak. “A Comparative Study of the Particle Filter and the Ensemble Kalman Filter.” 2009. Thesis, University of Waterloo. Accessed July 23, 2019. http://hdl.handle.net/10012/4503.

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

MLA Handbook (7th Edition):

Datta Gupta, Syamantak. “A Comparative Study of the Particle Filter and the Ensemble Kalman Filter.” 2009. Web. 23 Jul 2019.

Vancouver:

Datta Gupta S. A Comparative Study of the Particle Filter and the Ensemble Kalman Filter. [Internet] [Thesis]. University of Waterloo; 2009. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10012/4503.

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

Council of Science Editors:

Datta Gupta S. A Comparative Study of the Particle Filter and the Ensemble Kalman Filter. [Thesis]. University of Waterloo; 2009. Available from: http://hdl.handle.net/10012/4503

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


University of Guelph

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

Degree: 2012, University of Guelph

 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 (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Dobbs, Angie. “Issues of Computational Efficiency and Model Approximation for Spatial Individual-Level Infectious Disease Models .” 2012. Thesis, University of Guelph. Accessed July 23, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3248.

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

MLA Handbook (7th Edition):

Dobbs, Angie. “Issues of Computational Efficiency and Model Approximation for Spatial Individual-Level Infectious Disease Models .” 2012. Web. 23 Jul 2019.

Vancouver:

Dobbs A. Issues of Computational Efficiency and Model Approximation for Spatial Individual-Level Infectious Disease Models . [Internet] [Thesis]. University of Guelph; 2012. [cited 2019 Jul 23]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3248.

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

Council of Science Editors:

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

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


University of Otago

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

Degree: 2013, University of Otago

 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)

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

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APA (6th 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 (16th Edition):

Ma, Erfang. “Application of Markov Chain Monte Carlo Methods in Electrical Impedance Tomography .” 2013. Doctoral Dissertation, University of Otago. Accessed July 23, 2019. http://hdl.handle.net/10523/4045.

MLA Handbook (7th Edition):

Ma, Erfang. “Application of Markov Chain Monte Carlo Methods in Electrical Impedance Tomography .” 2013. Web. 23 Jul 2019.

Vancouver:

Ma E. Application of Markov Chain Monte Carlo Methods in Electrical Impedance Tomography . [Internet] [Doctoral dissertation]. University of Otago; 2013. [cited 2019 Jul 23]. 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


University of Bath

21. Loza Reyes, Elisa. Classification of phylogenetic data via Bayesian mixture modelling.

Degree: PhD, 2010, University of Bath

 Conventional probabilistic models for phylogenetic inference assume that an evolutionary tree,andasinglesetofbranchlengthsandstochasticprocessofDNA evolutionare sufficient to characterise the generating process across an entire DNA alignment. Unfortunately such… (more)

Subjects/Keywords: 519; Markov chain Monte Carlo (MCMC); Bayesian inference; phylogenetic; mixture model

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APA (6th Edition):

Loza Reyes, E. (2010). Classification of phylogenetic data via Bayesian mixture modelling. (Doctoral Dissertation). University of Bath. Retrieved from https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916

Chicago Manual of Style (16th Edition):

Loza Reyes, Elisa. “Classification of phylogenetic data via Bayesian mixture modelling.” 2010. Doctoral Dissertation, University of Bath. Accessed July 23, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916.

MLA Handbook (7th Edition):

Loza Reyes, Elisa. “Classification of phylogenetic data via Bayesian mixture modelling.” 2010. Web. 23 Jul 2019.

Vancouver:

Loza Reyes E. Classification of phylogenetic data via Bayesian mixture modelling. [Internet] [Doctoral dissertation]. University of Bath; 2010. [cited 2019 Jul 23]. Available from: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916.

Council of Science Editors:

Loza Reyes E. Classification of phylogenetic data via Bayesian mixture modelling. [Doctoral Dissertation]. University of Bath; 2010. Available from: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916


University of Texas – Austin

22. Zhang, Michael Minyi. Distributed inference in Bayesian nonparametric models using partially collapsed MCMC: Distributed inference in Bayesian nonparametric models using partially collapsed Markov chain Monte Carlo.

Degree: Statistics, 2016, University of Texas – Austin

Bayesian nonparametric based models are an elegant way for discovering underlying latent features within a data set, but inference in such models can be slow.… (more)

Subjects/Keywords: Bayesian nonparametrics; MCMC; Distributed inference; Markov chain Monte Carlo

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APA (6th Edition):

Zhang, M. M. (2016). Distributed inference in Bayesian nonparametric models using partially collapsed MCMC: Distributed inference in Bayesian nonparametric models using partially collapsed Markov chain Monte Carlo. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/41630

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

Chicago Manual of Style (16th Edition):

Zhang, Michael Minyi. “Distributed inference in Bayesian nonparametric models using partially collapsed MCMC: Distributed inference in Bayesian nonparametric models using partially collapsed Markov chain Monte Carlo.” 2016. Thesis, University of Texas – Austin. Accessed July 23, 2019. http://hdl.handle.net/2152/41630.

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

MLA Handbook (7th Edition):

Zhang, Michael Minyi. “Distributed inference in Bayesian nonparametric models using partially collapsed MCMC: Distributed inference in Bayesian nonparametric models using partially collapsed Markov chain Monte Carlo.” 2016. Web. 23 Jul 2019.

Vancouver:

Zhang MM. Distributed inference in Bayesian nonparametric models using partially collapsed MCMC: Distributed inference in Bayesian nonparametric models using partially collapsed Markov chain Monte Carlo. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/2152/41630.

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

Council of Science Editors:

Zhang MM. Distributed inference in Bayesian nonparametric models using partially collapsed MCMC: Distributed inference in Bayesian nonparametric models using partially collapsed Markov chain Monte Carlo. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/41630

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


University of Michigan

23. Park, Joon Ha. Computational Inference Algorithms for Spatiotemporal Processes and Other Complex Models.

Degree: PhD, Statistics, 2018, University of Michigan

 Data analysis can be carried out based on a stochastic model that reflects the analyst's understanding of how the system in question behaves. The stochastic(more)

Subjects/Keywords: computational inference; spatiotemporal process; partially observed Markov process; sequential Monte Carlo; Markov chain Monte Carlo; Statistics and Numeric Data; Science

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APA (6th Edition):

Park, J. H. (2018). Computational Inference Algorithms for Spatiotemporal Processes and Other Complex Models. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/145801

Chicago Manual of Style (16th Edition):

Park, Joon Ha. “Computational Inference Algorithms for Spatiotemporal Processes and Other Complex Models.” 2018. Doctoral Dissertation, University of Michigan. Accessed July 23, 2019. http://hdl.handle.net/2027.42/145801.

MLA Handbook (7th Edition):

Park, Joon Ha. “Computational Inference Algorithms for Spatiotemporal Processes and Other Complex Models.” 2018. Web. 23 Jul 2019.

Vancouver:

Park JH. Computational Inference Algorithms for Spatiotemporal Processes and Other Complex Models. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/2027.42/145801.

Council of Science Editors:

Park JH. Computational Inference Algorithms for Spatiotemporal Processes and Other Complex Models. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/145801

24. Chen, Yuting. Inférence bayésienne dans les modèles de croissance de plantes pour la prévision et la caractérisation des incertitudes : Bayesian inference in plant growth models for prediction and uncertainty assessment.

Degree: Docteur es, Mathématiques appliquées, 2014, Châtenay-Malabry, Ecole centrale de Paris

 La croissance des plantes en interaction avec l'environnement peut être décrite par des modèles mathématiques. Ceux-ci présentent des perspectives prometteuses pour un nombre considérable d'applications… (more)

Subjects/Keywords: Chaînes de Markov; Méthode de Monte-Carlo séquentielle; Méthodes Bayésienne; Markov chains; Sequential Monte Carlo methods; Bayesian methods

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APA (6th Edition):

Chen, Y. (2014). Inférence bayésienne dans les modèles de croissance de plantes pour la prévision et la caractérisation des incertitudes : Bayesian inference in plant growth models for prediction and uncertainty assessment. (Doctoral Dissertation). Châtenay-Malabry, Ecole centrale de Paris. Retrieved from http://www.theses.fr/2014ECAP0040

Chicago Manual of Style (16th Edition):

Chen, Yuting. “Inférence bayésienne dans les modèles de croissance de plantes pour la prévision et la caractérisation des incertitudes : Bayesian inference in plant growth models for prediction and uncertainty assessment.” 2014. Doctoral Dissertation, Châtenay-Malabry, Ecole centrale de Paris. Accessed July 23, 2019. http://www.theses.fr/2014ECAP0040.

MLA Handbook (7th Edition):

Chen, Yuting. “Inférence bayésienne dans les modèles de croissance de plantes pour la prévision et la caractérisation des incertitudes : Bayesian inference in plant growth models for prediction and uncertainty assessment.” 2014. Web. 23 Jul 2019.

Vancouver:

Chen Y. Inférence bayésienne dans les modèles de croissance de plantes pour la prévision et la caractérisation des incertitudes : Bayesian inference in plant growth models for prediction and uncertainty assessment. [Internet] [Doctoral dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2014. [cited 2019 Jul 23]. Available from: http://www.theses.fr/2014ECAP0040.

Council of Science Editors:

Chen Y. Inférence bayésienne dans les modèles de croissance de plantes pour la prévision et la caractérisation des incertitudes : Bayesian inference in plant growth models for prediction and uncertainty assessment. [Doctoral Dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2014. Available from: http://www.theses.fr/2014ECAP0040


Virginia Tech

25. Carzolio, Marcos Arantes. On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions.

Degree: PhD, Statistics, 2016, Virginia Tech

 We are entering an exciting era, rich in the availability of data via sources such as the Internet, satellites, particle colliders, telecommunication networks, computer simulations,… (more)

Subjects/Keywords: Markov chain Monte Carlo; reversible jump; weighted particle tempering

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APA (6th Edition):

Carzolio, M. A. (2016). On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71765

Chicago Manual of Style (16th Edition):

Carzolio, Marcos Arantes. “On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions.” 2016. Doctoral Dissertation, Virginia Tech. Accessed July 23, 2019. http://hdl.handle.net/10919/71765.

MLA Handbook (7th Edition):

Carzolio, Marcos Arantes. “On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions.” 2016. Web. 23 Jul 2019.

Vancouver:

Carzolio MA. On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10919/71765.

Council of Science Editors:

Carzolio MA. On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71765


INP Toulouse

26. Wei, Qi. Bayesian fusion of multi-band images : A powerful tool for super-resolution : Fusion Bayésienne des multi-bandes Images : Un outil puissant pour la Super-résolution.

Degree: Docteur es, Signal, Image, Acoustique et Optimisation, 2015, INP Toulouse

 L’imagerie hyperspectrale (HS) consiste à acquérir une même scène dans plusieurs centaines de bandes spectrales contiguës (dimensions d'un cube de données), ce qui a conduit… (more)

Subjects/Keywords: Imagerie hyperspectrale; Fusion d'images; Démélange spectral; Problèmes inverses; Inférence Bayésienne; Méthode de Monte-Carlo par chaînes de Markov; Optimisation; Représentation parcimonieuse; Equation de Sylvester; Hyperspectral image; Image fusion; Spectral unmixing; Inverse problems; Bayesian inference; Markov Chain Monte Carlo methods; Optimization; Sparse representation; Sylvester equation

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APA (6th Edition):

Wei, Q. (2015). Bayesian fusion of multi-band images : A powerful tool for super-resolution : Fusion Bayésienne des multi-bandes Images : Un outil puissant pour la Super-résolution. (Doctoral Dissertation). INP Toulouse. Retrieved from http://www.theses.fr/2015INPT0059

Chicago Manual of Style (16th Edition):

Wei, Qi. “Bayesian fusion of multi-band images : A powerful tool for super-resolution : Fusion Bayésienne des multi-bandes Images : Un outil puissant pour la Super-résolution.” 2015. Doctoral Dissertation, INP Toulouse. Accessed July 23, 2019. http://www.theses.fr/2015INPT0059.

MLA Handbook (7th Edition):

Wei, Qi. “Bayesian fusion of multi-band images : A powerful tool for super-resolution : Fusion Bayésienne des multi-bandes Images : Un outil puissant pour la Super-résolution.” 2015. Web. 23 Jul 2019.

Vancouver:

Wei Q. Bayesian fusion of multi-band images : A powerful tool for super-resolution : Fusion Bayésienne des multi-bandes Images : Un outil puissant pour la Super-résolution. [Internet] [Doctoral dissertation]. INP Toulouse; 2015. [cited 2019 Jul 23]. Available from: http://www.theses.fr/2015INPT0059.

Council of Science Editors:

Wei Q. Bayesian fusion of multi-band images : A powerful tool for super-resolution : Fusion Bayésienne des multi-bandes Images : Un outil puissant pour la Super-résolution. [Doctoral Dissertation]. INP Toulouse; 2015. Available from: http://www.theses.fr/2015INPT0059


KTH

27. PAUL, DEBDAS. Efficient Parameter Inference for Stochastic Chemical Kinetics.

Degree: CB, 2014, KTH

  Parameter inference for stochastic systems is considered as one of the fundamental classical problems in the domain of computational systems biology. The problem becomes… (more)

Subjects/Keywords: stochastic chemical kinetics; systems biology; parameter inference; Markov Chain Monte Carlo; Chemical Master Equation; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi)

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APA (6th Edition):

PAUL, D. (2014). Efficient Parameter Inference for Stochastic Chemical Kinetics. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146869

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

Chicago Manual of Style (16th Edition):

PAUL, DEBDAS. “Efficient Parameter Inference for Stochastic Chemical Kinetics.” 2014. Thesis, KTH. Accessed July 23, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146869.

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

MLA Handbook (7th Edition):

PAUL, DEBDAS. “Efficient Parameter Inference for Stochastic Chemical Kinetics.” 2014. Web. 23 Jul 2019.

Vancouver:

PAUL D. Efficient Parameter Inference for Stochastic Chemical Kinetics. [Internet] [Thesis]. KTH; 2014. [cited 2019 Jul 23]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146869.

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

Council of Science Editors:

PAUL D. Efficient Parameter Inference for Stochastic Chemical Kinetics. [Thesis]. KTH; 2014. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146869

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


University of Plymouth

28. Shahtahmassebi, Golnaz. Bayesian modelling of ultra high-frequency financial data.

Degree: PhD, 2011, University of Plymouth

 The availability of ultra high-frequency (UHF) data on transactions has revolutionised data processing and statistical modelling techniques in finance. The unique characteristics of such data,… (more)

Subjects/Keywords: 658.05; ultra high-frequency, Bayesian, zero inflated Poisson difference, off-line, Markov chain Monte Carlo, Sequential Monte Carlo, particle filters, online, FTSE100, probability integral transform, sequential deviance information criterion, generalised Poisson difference

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APA (6th Edition):

Shahtahmassebi, G. (2011). Bayesian modelling of ultra high-frequency financial data. (Doctoral Dissertation). University of Plymouth. Retrieved from http://hdl.handle.net/10026.1/894

Chicago Manual of Style (16th Edition):

Shahtahmassebi, Golnaz. “Bayesian modelling of ultra high-frequency financial data.” 2011. Doctoral Dissertation, University of Plymouth. Accessed July 23, 2019. http://hdl.handle.net/10026.1/894.

MLA Handbook (7th Edition):

Shahtahmassebi, Golnaz. “Bayesian modelling of ultra high-frequency financial data.” 2011. Web. 23 Jul 2019.

Vancouver:

Shahtahmassebi G. Bayesian modelling of ultra high-frequency financial data. [Internet] [Doctoral dissertation]. University of Plymouth; 2011. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/10026.1/894.

Council of Science Editors:

Shahtahmassebi G. Bayesian modelling of ultra high-frequency financial data. [Doctoral Dissertation]. University of Plymouth; 2011. Available from: http://hdl.handle.net/10026.1/894


University of Cambridge

29. Javid, Kamran. Physical modelling of galaxy clusters and Bayesian inference in astrophysics .

Degree: 2019, University of Cambridge

 This thesis is concerned with the modelling of galaxy clusters, applying these models to real and simulated data using Bayesian inference, and the development of… (more)

Subjects/Keywords: Mathematical modelling; Computational Astrophysics; Computational Statistics; Bayesian inference; Applied statistics; Monte Carlo methods; Markov Chain Monte Carlo; Data analysis; Bayesian statistics; Galaxy cluster modelling

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

APA (6th Edition):

Javid, K. (2019). Physical modelling of galaxy clusters and Bayesian inference in astrophysics . (Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/293473

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

Chicago Manual of Style (16th Edition):

Javid, Kamran. “Physical modelling of galaxy clusters and Bayesian inference in astrophysics .” 2019. Thesis, University of Cambridge. Accessed July 23, 2019. https://www.repository.cam.ac.uk/handle/1810/293473.

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

MLA Handbook (7th Edition):

Javid, Kamran. “Physical modelling of galaxy clusters and Bayesian inference in astrophysics .” 2019. Web. 23 Jul 2019.

Vancouver:

Javid K. Physical modelling of galaxy clusters and Bayesian inference in astrophysics . [Internet] [Thesis]. University of Cambridge; 2019. [cited 2019 Jul 23]. Available from: https://www.repository.cam.ac.uk/handle/1810/293473.

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

Council of Science Editors:

Javid K. Physical modelling of galaxy clusters and Bayesian inference in astrophysics . [Thesis]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/293473

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

30. Yun, Jong Hyun. Ensemble filtering for state space models.

Degree: PhD, 0329, 2012, University of Illinois – Urbana-Champaign

 The state space model has been widely used in various fields including economics, finance, bioinformatics, oceanography, and tomography. The goal of the filtering problem is… (more)

Subjects/Keywords: Nonlinear filtering; Sequential Monte Carlo; Particle filter; Ensemble Kalman filter; State space model; Target tracking; Augmented particle filter; Localized augmented particle filter; Particle Monte Carlo Markov chain; Lorenz model; Particle filtering with independent batches.

…through the state equation that describes the first order Markov chain. The xt ’s are… …Filters The particle filter (PF), also known as sequential importance sampling (… …is not restricted to Sequential Monte Carlo (SMC) or state space models (… …the particle filter, which is presented in Section 1.3. 1.2 Kalman Filters When both the… …results can be found in Doucet et al. (2000). The performance of the particle filter… 

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

APA (6th Edition):

Yun, J. H. (2012). Ensemble filtering for state space models. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/34574

Chicago Manual of Style (16th Edition):

Yun, Jong Hyun. “Ensemble filtering for state space models.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed July 23, 2019. http://hdl.handle.net/2142/34574.

MLA Handbook (7th Edition):

Yun, Jong Hyun. “Ensemble filtering for state space models.” 2012. Web. 23 Jul 2019.

Vancouver:

Yun JH. Ensemble filtering for state space models. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2019 Jul 23]. Available from: http://hdl.handle.net/2142/34574.

Council of Science Editors:

Yun JH. Ensemble filtering for state space models. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/34574

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