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You searched for subject:(Sequential Monte Carlo). Showing records 1 – 30 of 108 total matches.

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University of New South Wales

1. 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 (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 June 25, 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. 25 Jun 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 Jun 25]. 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 Southern California

2. Stram, Alexander H. Theoretical foundations of approximate Bayesian computation.

Degree: MA, Mathematics, 2015, University of Southern California

 We introduce Monte Carlo estimates with discussion of numerical integration and the curse of dimensionality, using a toy example of estimating π using a d−dimensional… (more)

Subjects/Keywords: approximate Bayesian computation; Monte Carlo; sequential Monte Carlo; Bayesian statistics; importance sampling; sequential importance sampling

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APA (6th Edition):

Stram, A. H. (2015). Theoretical foundations of approximate Bayesian computation. (Masters Thesis). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/558978/rec/7417

Chicago Manual of Style (16th Edition):

Stram, Alexander H. “Theoretical foundations of approximate Bayesian computation.” 2015. Masters Thesis, University of Southern California. Accessed June 25, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/558978/rec/7417.

MLA Handbook (7th Edition):

Stram, Alexander H. “Theoretical foundations of approximate Bayesian computation.” 2015. Web. 25 Jun 2019.

Vancouver:

Stram AH. Theoretical foundations of approximate Bayesian computation. [Internet] [Masters thesis]. University of Southern California; 2015. [cited 2019 Jun 25]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/558978/rec/7417.

Council of Science Editors:

Stram AH. Theoretical foundations of approximate Bayesian computation. [Masters Thesis]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/558978/rec/7417

3. Buchholz, Alexander. High dimensional Bayesian computation : Computation bayésienne en grande dimension.

Degree: Docteur es, Mathématiques appliquées, 2018, Paris Saclay

La statistique bayésienne computationnelle construit des approximations de la distribution a posteriori soit par échantillonnage, soit en construisant des approximations tractables. La contribution de cette… (more)

Subjects/Keywords: Monte Carlo sequentiel; Statistique bayesienne; Quasi Monte Carlo; Sequential Monte Carlo; Bayesian statistics; Quasi Monte Carlo; 519;

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APA (6th Edition):

Buchholz, A. (2018). High dimensional Bayesian computation : Computation bayésienne en grande dimension. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2018SACLG004

Chicago Manual of Style (16th Edition):

Buchholz, Alexander. “High dimensional Bayesian computation : Computation bayésienne en grande dimension.” 2018. Doctoral Dissertation, Paris Saclay. Accessed June 25, 2019. http://www.theses.fr/2018SACLG004.

MLA Handbook (7th Edition):

Buchholz, Alexander. “High dimensional Bayesian computation : Computation bayésienne en grande dimension.” 2018. Web. 25 Jun 2019.

Vancouver:

Buchholz A. High dimensional Bayesian computation : Computation bayésienne en grande dimension. [Internet] [Doctoral dissertation]. Paris Saclay; 2018. [cited 2019 Jun 25]. Available from: http://www.theses.fr/2018SACLG004.

Council of Science Editors:

Buchholz A. High dimensional Bayesian computation : Computation bayésienne en grande dimension. [Doctoral Dissertation]. Paris Saclay; 2018. Available from: http://www.theses.fr/2018SACLG004

4. Feliot, Paul. Une approche Bayésienne pour l'optimisation multi-objectif sous contraintes : A Bayesian approach to constrained multi-objective optimization.

Degree: Docteur es, Traitement du signal et des images, 2017, Paris Saclay

Ces travaux de thèse portent sur l'optimisation multi-objectif de fonctions à valeurs réelles sous contraintes d'inégalités. En particulier, nous nous intéressons à des problèmes pour… (more)

Subjects/Keywords: Optimisation Bayésienne; Multi-objectif; Monte Carlo séquentiel; Bayesian optimization; Smulti-objective; Sequential Monte Carlo

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APA (6th Edition):

Feliot, P. (2017). Une approche Bayésienne pour l'optimisation multi-objectif sous contraintes : A Bayesian approach to constrained multi-objective optimization. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2017SACLC045

Chicago Manual of Style (16th Edition):

Feliot, Paul. “Une approche Bayésienne pour l'optimisation multi-objectif sous contraintes : A Bayesian approach to constrained multi-objective optimization.” 2017. Doctoral Dissertation, Paris Saclay. Accessed June 25, 2019. http://www.theses.fr/2017SACLC045.

MLA Handbook (7th Edition):

Feliot, Paul. “Une approche Bayésienne pour l'optimisation multi-objectif sous contraintes : A Bayesian approach to constrained multi-objective optimization.” 2017. Web. 25 Jun 2019.

Vancouver:

Feliot P. Une approche Bayésienne pour l'optimisation multi-objectif sous contraintes : A Bayesian approach to constrained multi-objective optimization. [Internet] [Doctoral dissertation]. Paris Saclay; 2017. [cited 2019 Jun 25]. Available from: http://www.theses.fr/2017SACLC045.

Council of Science Editors:

Feliot P. Une approche Bayésienne pour l'optimisation multi-objectif sous contraintes : A Bayesian approach to constrained multi-objective optimization. [Doctoral Dissertation]. Paris Saclay; 2017. Available from: http://www.theses.fr/2017SACLC045

5. Shatskikh, Katherine. Epidemic Detection in Two Populations.

Degree: 2017, University of California – eScholarship, University of California

 Traditional epidemic detection algorithms make decisions using only local information. We propose a novel approach that explicitly models spatial information fusion from several meta-populations. Our… (more)

Subjects/Keywords: Statistics; Epidemiology; quickest detection; regression monte carlo; sequential regression monte carlo; stochastic epidemiological models

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APA (6th Edition):

Shatskikh, K. (2017). Epidemic Detection in Two Populations. (Thesis). University of California – eScholarship, University of California. Retrieved from http://www.escholarship.org/uc/item/4x1556mb

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):

Shatskikh, Katherine. “Epidemic Detection in Two Populations.” 2017. Thesis, University of California – eScholarship, University of California. Accessed June 25, 2019. http://www.escholarship.org/uc/item/4x1556mb.

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

MLA Handbook (7th Edition):

Shatskikh, Katherine. “Epidemic Detection in Two Populations.” 2017. Web. 25 Jun 2019.

Vancouver:

Shatskikh K. Epidemic Detection in Two Populations. [Internet] [Thesis]. University of California – eScholarship, University of California; 2017. [cited 2019 Jun 25]. Available from: http://www.escholarship.org/uc/item/4x1556mb.

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

Council of Science Editors:

Shatskikh K. Epidemic Detection in Two Populations. [Thesis]. University of California – eScholarship, University of California; 2017. Available from: http://www.escholarship.org/uc/item/4x1556mb

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


University of Melbourne

6. 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 (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 June 25, 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. 25 Jun 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 Jun 25]. 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

7. 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

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)

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 (6th 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 (16th 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 June 25, 2019. http://www.theses.fr/2019PAUU3005.

MLA Handbook (7th Edition):

Minvielle-Larrousse, Pierre. “Méthodes de simulation stochastique pour le traitement de l’information : Stochastic simulation methods for information processing.” 2019. Web. 25 Jun 2019.

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 2019 Jun 25]. 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


Cleveland State University

8. Tumuluri, Uma. Nonlinear State Estimation in Polymer Electrolyte Membrane Fuel Cells.

Degree: MSin Chemical Engineering, Fenn College of Engineering, 2008, Cleveland State University

 Research on alternative and renewable energy sources which are amicable to the environment has gained momentum because of the growing concern about the tremendous increase… (more)

Subjects/Keywords: FUEL CELLS; sequential Monte Carlo; unscented Kalman filter

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APA (6th Edition):

Tumuluri, U. (2008). Nonlinear State Estimation in Polymer Electrolyte Membrane Fuel Cells. (Masters Thesis). Cleveland State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=csu1231961499

Chicago Manual of Style (16th Edition):

Tumuluri, Uma. “Nonlinear State Estimation in Polymer Electrolyte Membrane Fuel Cells.” 2008. Masters Thesis, Cleveland State University. Accessed June 25, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=csu1231961499.

MLA Handbook (7th Edition):

Tumuluri, Uma. “Nonlinear State Estimation in Polymer Electrolyte Membrane Fuel Cells.” 2008. Web. 25 Jun 2019.

Vancouver:

Tumuluri U. Nonlinear State Estimation in Polymer Electrolyte Membrane Fuel Cells. [Internet] [Masters thesis]. Cleveland State University; 2008. [cited 2019 Jun 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=csu1231961499.

Council of Science Editors:

Tumuluri U. Nonlinear State Estimation in Polymer Electrolyte Membrane Fuel Cells. [Masters Thesis]. Cleveland State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=csu1231961499


Virginia Tech

9. Hoegh, Andrew B. Predictive Model Fusion: A Modular Approach to Big, Unstructured Data.

Degree: PhD, Statistics, 2016, Virginia Tech

 Data sets of increasing size and complexity require new approaches for prediction as the sheer volume of data from disparate sources inhibits joint processing and… (more)

Subjects/Keywords: Model Fusion; Spatiotemporal Modeling; Areal Data; Sequential Monte Carlo

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APA (6th Edition):

Hoegh, A. B. (2016). Predictive Model Fusion: A Modular Approach to Big, Unstructured Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/70921

Chicago Manual of Style (16th Edition):

Hoegh, Andrew B. “Predictive Model Fusion: A Modular Approach to Big, Unstructured Data.” 2016. Doctoral Dissertation, Virginia Tech. Accessed June 25, 2019. http://hdl.handle.net/10919/70921.

MLA Handbook (7th Edition):

Hoegh, Andrew B. “Predictive Model Fusion: A Modular Approach to Big, Unstructured Data.” 2016. Web. 25 Jun 2019.

Vancouver:

Hoegh AB. Predictive Model Fusion: A Modular Approach to Big, Unstructured Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10919/70921.

Council of Science Editors:

Hoegh AB. Predictive Model Fusion: A Modular Approach to Big, Unstructured Data. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/70921


University of Cambridge

10. Yildirim, Sinan. Maximum likelihood parameter estimation in time series models using sequential Monte Carlo.

Degree: PhD, 2013, University of Cambridge

 Time series models are used to characterise uncertainty in many real-world dynamical phenomena. A time series model typically contains a static variable, called parameter, which… (more)

Subjects/Keywords: 519.5; Sequential Monte Carlo; Parameter estimation; Bayesian statistics

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APA (6th Edition):

Yildirim, S. (2013). Maximum likelihood parameter estimation in time series models using sequential Monte Carlo. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/244707 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590211

Chicago Manual of Style (16th Edition):

Yildirim, Sinan. “Maximum likelihood parameter estimation in time series models using sequential Monte Carlo.” 2013. Doctoral Dissertation, University of Cambridge. Accessed June 25, 2019. https://www.repository.cam.ac.uk/handle/1810/244707 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590211.

MLA Handbook (7th Edition):

Yildirim, Sinan. “Maximum likelihood parameter estimation in time series models using sequential Monte Carlo.” 2013. Web. 25 Jun 2019.

Vancouver:

Yildirim S. Maximum likelihood parameter estimation in time series models using sequential Monte Carlo. [Internet] [Doctoral dissertation]. University of Cambridge; 2013. [cited 2019 Jun 25]. Available from: https://www.repository.cam.ac.uk/handle/1810/244707 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590211.

Council of Science Editors:

Yildirim S. Maximum likelihood parameter estimation in time series models using sequential Monte Carlo. [Doctoral Dissertation]. University of Cambridge; 2013. Available from: https://www.repository.cam.ac.uk/handle/1810/244707 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590211


University of Illinois – Urbana-Champaign

11. Eisinger, Robert David. Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables.

Degree: PhD, Statistics, 2016, University of Illinois – Urbana-Champaign

 We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and high-dimensional tables. In each case, the proposals… (more)

Subjects/Keywords: Monte Carlo method; Sequential importance sampling; Counting problem; Contingency Table

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APA (6th Edition):

Eisinger, R. D. (2016). Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92928

Chicago Manual of Style (16th Edition):

Eisinger, Robert David. “Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed June 25, 2019. http://hdl.handle.net/2142/92928.

MLA Handbook (7th Edition):

Eisinger, Robert David. “Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables.” 2016. Web. 25 Jun 2019.

Vancouver:

Eisinger RD. Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2142/92928.

Council of Science Editors:

Eisinger RD. Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92928


University of Arizona

12. 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 June 25, 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. 25 Jun 2019.

Vancouver:

Woodard AJ. Bayesian Estimation of a Single Mass Concentration Within an Asteroid . [Internet] [Masters thesis]. University of Arizona; 2017. [cited 2019 Jun 25]. 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

13. 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 June 25, 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. 25 Jun 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 Jun 25]. 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


University of Plymouth

14. 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 (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 June 25, 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. 25 Jun 2019.

Vancouver:

Al-Saadony M. Bayesian stochastic differential equation modelling with application to finance. [Internet] [Doctoral dissertation]. University of Plymouth; 2013. [cited 2019 Jun 25]. 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

15. Ridgway, James. Advances in computational Bayesian statistics and the approximation of Gibbs measures : Avancées en statistiques computationelles Bayesiennes et approximation de mesures de Gibbs.

Degree: Docteur es, Mathématiques appliquées, 2015, Paris 9

Ce mémoire de thèse regroupe plusieurs méthodes de calcul d'estimateur en statistiques bayésiennes. Plusieurs approches d'estimation seront considérées dans ce manuscrit. D'abord en estimation nous… (more)

Subjects/Keywords: Méthodes de Monte-Carlo séquentielles; Echantillonnage de Gibbs; Vb; Sequential Monte Carlo; Gibbs sampling; Variationnal Bayes; 519.5

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APA (6th Edition):

Ridgway, J. (2015). Advances in computational Bayesian statistics and the approximation of Gibbs measures : Avancées en statistiques computationelles Bayesiennes et approximation de mesures de Gibbs. (Doctoral Dissertation). Paris 9. Retrieved from http://www.theses.fr/2015PA090030

Chicago Manual of Style (16th Edition):

Ridgway, James. “Advances in computational Bayesian statistics and the approximation of Gibbs measures : Avancées en statistiques computationelles Bayesiennes et approximation de mesures de Gibbs.” 2015. Doctoral Dissertation, Paris 9. Accessed June 25, 2019. http://www.theses.fr/2015PA090030.

MLA Handbook (7th Edition):

Ridgway, James. “Advances in computational Bayesian statistics and the approximation of Gibbs measures : Avancées en statistiques computationelles Bayesiennes et approximation de mesures de Gibbs.” 2015. Web. 25 Jun 2019.

Vancouver:

Ridgway J. Advances in computational Bayesian statistics and the approximation of Gibbs measures : Avancées en statistiques computationelles Bayesiennes et approximation de mesures de Gibbs. [Internet] [Doctoral dissertation]. Paris 9; 2015. [cited 2019 Jun 25]. Available from: http://www.theses.fr/2015PA090030.

Council of Science Editors:

Ridgway J. Advances in computational Bayesian statistics and the approximation of Gibbs measures : Avancées en statistiques computationelles Bayesiennes et approximation de mesures de Gibbs. [Doctoral Dissertation]. Paris 9; 2015. Available from: http://www.theses.fr/2015PA090030


University of Michigan

16. 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 June 25, 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. 25 Jun 2019.

Vancouver:

Park JH. Computational Inference Algorithms for Spatiotemporal Processes and Other Complex Models. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Jun 25]. 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


University of Oxford

17. Heng, Jeremy. On the use of transport and optimal control methods for Monte Carlo simulation.

Degree: PhD, 2016, University of Oxford

 This thesis explores ideas from transport theory and optimal control to develop novel Monte Carlo methods to perform efficient statistical computation. The first project considers… (more)

Subjects/Keywords: Transport theory – Statistical methods; Optimal control; Normalizing constants; Sequential Monte Carlo; Approximate dynamic programming; Monte Carlo; Mass transport; Annealed importance sampling; Reinforcement learning; Markov chain Monte Carlo

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APA (6th Edition):

Heng, J. (2016). On the use of transport and optimal control methods for Monte Carlo simulation. (Doctoral Dissertation). University of Oxford. Retrieved from https://ora.ox.ac.uk/objects/uuid:6cbc7690-ac54-4a6a-b235-57fa62e5b2fc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740797

Chicago Manual of Style (16th Edition):

Heng, Jeremy. “On the use of transport and optimal control methods for Monte Carlo simulation.” 2016. Doctoral Dissertation, University of Oxford. Accessed June 25, 2019. https://ora.ox.ac.uk/objects/uuid:6cbc7690-ac54-4a6a-b235-57fa62e5b2fc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740797.

MLA Handbook (7th Edition):

Heng, Jeremy. “On the use of transport and optimal control methods for Monte Carlo simulation.” 2016. Web. 25 Jun 2019.

Vancouver:

Heng J. On the use of transport and optimal control methods for Monte Carlo simulation. [Internet] [Doctoral dissertation]. University of Oxford; 2016. [cited 2019 Jun 25]. Available from: https://ora.ox.ac.uk/objects/uuid:6cbc7690-ac54-4a6a-b235-57fa62e5b2fc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740797.

Council of Science Editors:

Heng J. On the use of transport and optimal control methods for Monte Carlo simulation. [Doctoral Dissertation]. University of Oxford; 2016. Available from: https://ora.ox.ac.uk/objects/uuid:6cbc7690-ac54-4a6a-b235-57fa62e5b2fc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740797


The Ohio State University

18. Schneider, Grant W. Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization.

Degree: PhD, Statistics, 2014, The Ohio State University

 Stochastic differential equations (SDEs) are used as statistical models in many disciplines. However, intractable likelihood functions for SDEs make inference challenging, and we need to… (more)

Subjects/Keywords: Statistics; Discretely sampled diffusions; Expected improvement; Gaussian process; Sequential Monte Carlo; Parameter estimation

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APA (6th Edition):

Schneider, G. W. (2014). Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1406912247

Chicago Manual of Style (16th Edition):

Schneider, Grant W. “Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization.” 2014. Doctoral Dissertation, The Ohio State University. Accessed June 25, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406912247.

MLA Handbook (7th Edition):

Schneider, Grant W. “Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization.” 2014. Web. 25 Jun 2019.

Vancouver:

Schneider GW. Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization. [Internet] [Doctoral dissertation]. The Ohio State University; 2014. [cited 2019 Jun 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406912247.

Council of Science Editors:

Schneider GW. Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging-Based Optimization. [Doctoral Dissertation]. The Ohio State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406912247


Georgia State University

19. Gu, Feng. Dynamic Data Driven Application System for Wildfire Spread Simulation.

Degree: PhD, Computer Science, 2010, Georgia State University

 Wildfires have significant impact on both ecosystems and human society. To effectively manage wildfires, simulation models are used to study and predict wildfire spread. The… (more)

Subjects/Keywords: Wildfire spread; Modeling; Simulation; DEVS; DDDAS; Sequential Monte Carlo methods; Computer Sciences

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

APA (6th Edition):

Gu, F. (2010). Dynamic Data Driven Application System for Wildfire Spread Simulation. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_diss/57

Chicago Manual of Style (16th Edition):

Gu, Feng. “Dynamic Data Driven Application System for Wildfire Spread Simulation.” 2010. Doctoral Dissertation, Georgia State University. Accessed June 25, 2019. https://scholarworks.gsu.edu/cs_diss/57.

MLA Handbook (7th Edition):

Gu, Feng. “Dynamic Data Driven Application System for Wildfire Spread Simulation.” 2010. Web. 25 Jun 2019.

Vancouver:

Gu F. Dynamic Data Driven Application System for Wildfire Spread Simulation. [Internet] [Doctoral dissertation]. Georgia State University; 2010. [cited 2019 Jun 25]. Available from: https://scholarworks.gsu.edu/cs_diss/57.

Council of Science Editors:

Gu F. Dynamic Data Driven Application System for Wildfire Spread Simulation. [Doctoral Dissertation]. Georgia State University; 2010. Available from: https://scholarworks.gsu.edu/cs_diss/57


Georgia State University

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

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 June 25, 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. 25 Jun 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 Jun 25]. 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


University of Tennessee – Knoxville

21. To, Gary. Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions.

Degree: 2012, University of Tennessee – Knoxville

 This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering,… (more)

Subjects/Keywords: Motion Tracking; Sequential Monte Carlo; hyper-dimensional statistic; Biomedical Devices and Instrumentation

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APA (6th Edition):

To, G. (2012). Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/1592

Chicago Manual of Style (16th Edition):

To, Gary. “Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions.” 2012. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed June 25, 2019. https://trace.tennessee.edu/utk_graddiss/1592.

MLA Handbook (7th Edition):

To, Gary. “Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions.” 2012. Web. 25 Jun 2019.

Vancouver:

To G. Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2012. [cited 2019 Jun 25]. Available from: https://trace.tennessee.edu/utk_graddiss/1592.

Council of Science Editors:

To G. Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2012. Available from: https://trace.tennessee.edu/utk_graddiss/1592


University of Tennessee – Knoxville

22. Kang, Kai. Advanced sequential Monte Carlo methods and their applications to sparse sensor network for detection and estimation.

Degree: 2016, University of Tennessee – Knoxville

 The general state space models present a flexible framework for modeling dynamic systems and therefore have vast applications in many disciplines such as engineering, economics,… (more)

Subjects/Keywords: sequential Monte Carlo; multi-target tracking; wireless sensor network; homotopy; Applied Statistics; Probability; Statistical Theory

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APA (6th Edition):

Kang, K. (2016). Advanced sequential Monte Carlo methods and their applications to sparse sensor network for detection and estimation. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/3933

Chicago Manual of Style (16th Edition):

Kang, Kai. “Advanced sequential Monte Carlo methods and their applications to sparse sensor network for detection and estimation.” 2016. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed June 25, 2019. https://trace.tennessee.edu/utk_graddiss/3933.

MLA Handbook (7th Edition):

Kang, Kai. “Advanced sequential Monte Carlo methods and their applications to sparse sensor network for detection and estimation.” 2016. Web. 25 Jun 2019.

Vancouver:

Kang K. Advanced sequential Monte Carlo methods and their applications to sparse sensor network for detection and estimation. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2016. [cited 2019 Jun 25]. Available from: https://trace.tennessee.edu/utk_graddiss/3933.

Council of Science Editors:

Kang K. Advanced sequential Monte Carlo methods and their applications to sparse sensor network for detection and estimation. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2016. Available from: https://trace.tennessee.edu/utk_graddiss/3933


University of California – Riverside

23. Gao, Chen. Metagenomic Binning Algorithms.

Degree: Applied Statistics, 2017, University of California – Riverside

 Metagenomics is the study of DNAs of microorganisms that are taken directly from environmental samples without cultivation and isolation. Recently, the emerging field of metagenome… (more)

Subjects/Keywords: Statistics; Bioinformatics; Binning algorithms; DirichletCluster; Dirichlet process; Metagenomics; Sequential Monte Carlo; Unsupervised

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

APA (6th Edition):

Gao, C. (2017). Metagenomic Binning Algorithms. (Thesis). University of California – Riverside. Retrieved from http://www.escholarship.org/uc/item/19j606dr

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):

Gao, Chen. “Metagenomic Binning Algorithms.” 2017. Thesis, University of California – Riverside. Accessed June 25, 2019. http://www.escholarship.org/uc/item/19j606dr.

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

MLA Handbook (7th Edition):

Gao, Chen. “Metagenomic Binning Algorithms.” 2017. Web. 25 Jun 2019.

Vancouver:

Gao C. Metagenomic Binning Algorithms. [Internet] [Thesis]. University of California – Riverside; 2017. [cited 2019 Jun 25]. Available from: http://www.escholarship.org/uc/item/19j606dr.

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

Council of Science Editors:

Gao C. Metagenomic Binning Algorithms. [Thesis]. University of California – Riverside; 2017. Available from: http://www.escholarship.org/uc/item/19j606dr

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


University of Manchester

24. Syrri, Angeliki Lydia anton Ioannis. RELIABILITY AND RISK ANALYSIS OF POST FAULT CAPACITY SERVICES IN SMART DISTRIBUTION NETWORKS.

Degree: 2017, University of Manchester

 Recent technological developments are bringing about substantial changes that are converting traditional distribution networks into “smart” distribution networks. In particular, it is possible to observe… (more)

Subjects/Keywords: demand response, distribution network planning, differentiated reliability, distributed energy resources; sequential monte carlo simulation

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APA (6th Edition):

Syrri, A. L. a. I. (2017). RELIABILITY AND RISK ANALYSIS OF POST FAULT CAPACITY SERVICES IN SMART DISTRIBUTION NETWORKS. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:307088

Chicago Manual of Style (16th Edition):

Syrri, Angeliki Lydia anton Ioannis. “RELIABILITY AND RISK ANALYSIS OF POST FAULT CAPACITY SERVICES IN SMART DISTRIBUTION NETWORKS.” 2017. Doctoral Dissertation, University of Manchester. Accessed June 25, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:307088.

MLA Handbook (7th Edition):

Syrri, Angeliki Lydia anton Ioannis. “RELIABILITY AND RISK ANALYSIS OF POST FAULT CAPACITY SERVICES IN SMART DISTRIBUTION NETWORKS.” 2017. Web. 25 Jun 2019.

Vancouver:

Syrri ALaI. RELIABILITY AND RISK ANALYSIS OF POST FAULT CAPACITY SERVICES IN SMART DISTRIBUTION NETWORKS. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Jun 25]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:307088.

Council of Science Editors:

Syrri ALaI. RELIABILITY AND RISK ANALYSIS OF POST FAULT CAPACITY SERVICES IN SMART DISTRIBUTION NETWORKS. [Doctoral Dissertation]. University of Manchester; 2017. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:307088


Duke University

25. Marion, Joseph. Finite Sample Bounds and Path Selection for Sequential Monte Carlo .

Degree: 2018, Duke University

Sequential Monte Carlo (SMC) samplers have received attention as an alternative to Markov chain Monte Carlo for Bayesian inference problems due to their strong… (more)

Subjects/Keywords: Statistics; Bayesian Statistics; Computational Completity; Finite Sample Bounds; Path Sampling; Path Selection; Sequential Monte Carlo

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APA (6th Edition):

Marion, J. (2018). Finite Sample Bounds and Path Selection for Sequential Monte Carlo . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/17515

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):

Marion, Joseph. “Finite Sample Bounds and Path Selection for Sequential Monte Carlo .” 2018. Thesis, Duke University. Accessed June 25, 2019. http://hdl.handle.net/10161/17515.

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

MLA Handbook (7th Edition):

Marion, Joseph. “Finite Sample Bounds and Path Selection for Sequential Monte Carlo .” 2018. Web. 25 Jun 2019.

Vancouver:

Marion J. Finite Sample Bounds and Path Selection for Sequential Monte Carlo . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/10161/17515.

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

Council of Science Editors:

Marion J. Finite Sample Bounds and Path Selection for Sequential Monte Carlo . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/17515

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


The Ohio State University

26. Lang, Lixin. Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science.

Degree: PhD, Statistics, 2008, The Ohio State University

  The research effort in this dissertation is targeted to investigate theoretical properties of some key statistics used in the sequential Monte Carlo (SMC) sampling,… (more)

Subjects/Keywords: Statistics; Sequential Monte Carlo; SMC; MCMC; Smoothing; Prior Checking; Predictive Density Value

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APA (6th Edition):

Lang, L. (2008). Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289

Chicago Manual of Style (16th Edition):

Lang, Lixin. “Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science.” 2008. Doctoral Dissertation, The Ohio State University. Accessed June 25, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289.

MLA Handbook (7th Edition):

Lang, Lixin. “Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science.” 2008. Web. 25 Jun 2019.

Vancouver:

Lang L. Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2019 Jun 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289.

Council of Science Editors:

Lang L. Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289


University of Adelaide

27. Haque, Qazi G M Ziaul. Bayesian estimation of monetary DSGE models and testing for indeterminacy.

Degree: 2018, University of Adelaide

 This thesis consists of three self-contained papers on U.S. monetary policy. The first paper examines monetary policy in the early 2000s, a prolonged period of… (more)

Subjects/Keywords: Research by publication; monetary policy; Taylor rules; indeterminacy; trend inflation; Great Inflation; Sequential Monte Carlo

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APA (6th Edition):

Haque, Q. G. M. Z. (2018). Bayesian estimation of monetary DSGE models and testing for indeterminacy. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/114258

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):

Haque, Qazi G M Ziaul. “Bayesian estimation of monetary DSGE models and testing for indeterminacy.” 2018. Thesis, University of Adelaide. Accessed June 25, 2019. http://hdl.handle.net/2440/114258.

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

MLA Handbook (7th Edition):

Haque, Qazi G M Ziaul. “Bayesian estimation of monetary DSGE models and testing for indeterminacy.” 2018. Web. 25 Jun 2019.

Vancouver:

Haque QGMZ. Bayesian estimation of monetary DSGE models and testing for indeterminacy. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/2440/114258.

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

Council of Science Editors:

Haque QGMZ. Bayesian estimation of monetary DSGE models and testing for indeterminacy. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/114258

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


University of Arkansas

28. Ellis, Michael. Sequential Inference for Hidden Markov Models.

Degree: MS, 2018, University of Arkansas

  In many applications data are collected sequentially in time with very short time intervals between observations. If one is interested in using new observations… (more)

Subjects/Keywords: Hidden Makrov Models; Sequential Monte Carlo Methods; State Space Models; Time Series Analysis; Applied Statistics

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APA (6th Edition):

Ellis, M. (2018). Sequential Inference for Hidden Markov Models. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2963

Chicago Manual of Style (16th Edition):

Ellis, Michael. “Sequential Inference for Hidden Markov Models.” 2018. Masters Thesis, University of Arkansas. Accessed June 25, 2019. https://scholarworks.uark.edu/etd/2963.

MLA Handbook (7th Edition):

Ellis, Michael. “Sequential Inference for Hidden Markov Models.” 2018. Web. 25 Jun 2019.

Vancouver:

Ellis M. Sequential Inference for Hidden Markov Models. [Internet] [Masters thesis]. University of Arkansas; 2018. [cited 2019 Jun 25]. Available from: https://scholarworks.uark.edu/etd/2963.

Council of Science Editors:

Ellis M. Sequential Inference for Hidden Markov Models. [Masters Thesis]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2963


University of Toronto

29. Shabany, Mahdi. VLSI Implementation of Digital Signal Processing Algorithms for MIMO/SISO Systems.

Degree: 2009, University of Toronto

The efficient high-throughput VLSI implementation of near-optimal multiple-input multiple-output (MIMO) detectors for 4x4 MIMO systems in high-order quadrature amplitude modulation (QAM) schemes has been a… (more)

Subjects/Keywords: ASIC Implementation; K-Best MIMO Detector; Sequential Monte Carlo; Lattice Reduction; 0544

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APA (6th Edition):

Shabany, M. (2009). VLSI Implementation of Digital Signal Processing Algorithms for MIMO/SISO Systems. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/17485

Chicago Manual of Style (16th Edition):

Shabany, Mahdi. “VLSI Implementation of Digital Signal Processing Algorithms for MIMO/SISO Systems.” 2009. Doctoral Dissertation, University of Toronto. Accessed June 25, 2019. http://hdl.handle.net/1807/17485.

MLA Handbook (7th Edition):

Shabany, Mahdi. “VLSI Implementation of Digital Signal Processing Algorithms for MIMO/SISO Systems.” 2009. Web. 25 Jun 2019.

Vancouver:

Shabany M. VLSI Implementation of Digital Signal Processing Algorithms for MIMO/SISO Systems. [Internet] [Doctoral dissertation]. University of Toronto; 2009. [cited 2019 Jun 25]. Available from: http://hdl.handle.net/1807/17485.

Council of Science Editors:

Shabany M. VLSI Implementation of Digital Signal Processing Algorithms for MIMO/SISO Systems. [Doctoral Dissertation]. University of Toronto; 2009. Available from: http://hdl.handle.net/1807/17485


University of Manchester

30. Syrri, Angeliki Lydia Antonia. Reliability and risk analysis of post fault capacity services in smart distribution networks.

Degree: PhD, 2017, University of Manchester

 Recent technological developments are bringing about substantial changes that are converting traditional distribution networks into "smart" distribution networks. In particular, it is possible to observe… (more)

Subjects/Keywords: 621.31; demand response; distribution network planning; differentiated reliability; distributed energy resources; sequential Monte Carlo simulation

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APA (6th Edition):

Syrri, A. L. A. (2017). Reliability and risk analysis of post fault capacity services in smart distribution networks. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/reliability-and-risk-analysis-of-post-fault-capacity-services-in-smart-distribution-networks(b1a93b49-d307-4561-800d-0a9944a7a577).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.713616

Chicago Manual of Style (16th Edition):

Syrri, Angeliki Lydia Antonia. “Reliability and risk analysis of post fault capacity services in smart distribution networks.” 2017. Doctoral Dissertation, University of Manchester. Accessed June 25, 2019. https://www.research.manchester.ac.uk/portal/en/theses/reliability-and-risk-analysis-of-post-fault-capacity-services-in-smart-distribution-networks(b1a93b49-d307-4561-800d-0a9944a7a577).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.713616.

MLA Handbook (7th Edition):

Syrri, Angeliki Lydia Antonia. “Reliability and risk analysis of post fault capacity services in smart distribution networks.” 2017. Web. 25 Jun 2019.

Vancouver:

Syrri ALA. Reliability and risk analysis of post fault capacity services in smart distribution networks. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Jun 25]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/reliability-and-risk-analysis-of-post-fault-capacity-services-in-smart-distribution-networks(b1a93b49-d307-4561-800d-0a9944a7a577).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.713616.

Council of Science Editors:

Syrri ALA. Reliability and risk analysis of post fault capacity services in smart distribution networks. [Doctoral Dissertation]. University of Manchester; 2017. Available from: https://www.research.manchester.ac.uk/portal/en/theses/reliability-and-risk-analysis-of-post-fault-capacity-services-in-smart-distribution-networks(b1a93b49-d307-4561-800d-0a9944a7a577).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.713616

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