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You searched for subject:(geometric ergodicity). Showing records 1 – 10 of 10 total matches.

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University of Minnesota

1. Johnson, Leif Thomas. Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics.

Degree: PhD, Statistics, 2011, University of Minnesota

 With the steady increase of affordable computing, more and more often analysts are turning to computationally intensive techniques like Markov chain Monte Carlo (MCMC). To… (more)

Subjects/Keywords: Geometric Ergodicity; Markov chain Monte Carlo; Random-walk Metropolis; Variable Transformation; Statistics

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

APA (6th Edition):

Johnson, L. T. (2011). Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/113140

Chicago Manual of Style (16th Edition):

Johnson, Leif Thomas. “Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics.” 2011. Doctoral Dissertation, University of Minnesota. Accessed January 17, 2021. http://purl.umn.edu/113140.

MLA Handbook (7th Edition):

Johnson, Leif Thomas. “Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics.” 2011. Web. 17 Jan 2021.

Vancouver:

Johnson LT. Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics. [Internet] [Doctoral dissertation]. University of Minnesota; 2011. [cited 2021 Jan 17]. Available from: http://purl.umn.edu/113140.

Council of Science Editors:

Johnson LT. Geometric ergodicity of a random-walk metorpolis algorithm via variable transformation and computer aided reasoning in statistics. [Doctoral Dissertation]. University of Minnesota; 2011. Available from: http://purl.umn.edu/113140


Iowa State University

2. Dixit, Anand Ulhas. Developments in MCMC diagnostics and sparse Bayesian learning models.

Degree: 2018, Iowa State University

 This dissertation consists of three research articles on the topic of Markov chain Monte Carlo (MCMC) diagnostics and sparse Bayesian learning models. The first article… (more)

Subjects/Keywords: Geometric ergodicity; Kullback Leibler divergence; Monte Carlo standard error; Posterior impropriety; Relevance vector machine; Reproducing kernel Hilbert spaces; Statistics and Probability

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APA (6th Edition):

Dixit, A. U. (2018). Developments in MCMC diagnostics and sparse Bayesian learning models. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/17175

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):

Dixit, Anand Ulhas. “Developments in MCMC diagnostics and sparse Bayesian learning models.” 2018. Thesis, Iowa State University. Accessed January 17, 2021. https://lib.dr.iastate.edu/etd/17175.

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

MLA Handbook (7th Edition):

Dixit, Anand Ulhas. “Developments in MCMC diagnostics and sparse Bayesian learning models.” 2018. Web. 17 Jan 2021.

Vancouver:

Dixit AU. Developments in MCMC diagnostics and sparse Bayesian learning models. [Internet] [Thesis]. Iowa State University; 2018. [cited 2021 Jan 17]. Available from: https://lib.dr.iastate.edu/etd/17175.

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

Council of Science Editors:

Dixit AU. Developments in MCMC diagnostics and sparse Bayesian learning models. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/17175

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


University of Florida

3. Jung, Yeun Ji. Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors.

Degree: PhD, Statistics, 2015, University of Florida

Subjects/Keywords: bayesian; convergence; da; ergodicity; geometric; mcmc; regression

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

APA (6th Edition):

Jung, Y. J. (2015). Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0049518

Chicago Manual of Style (16th Edition):

Jung, Yeun Ji. “Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors.” 2015. Doctoral Dissertation, University of Florida. Accessed January 17, 2021. https://ufdc.ufl.edu/UFE0049518.

MLA Handbook (7th Edition):

Jung, Yeun Ji. “Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors.” 2015. Web. 17 Jan 2021.

Vancouver:

Jung YJ. Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2021 Jan 17]. Available from: https://ufdc.ufl.edu/UFE0049518.

Council of Science Editors:

Jung YJ. Convergence Analysis of Markov Chain Monte Carlo Algorithms for Bayesian Regression Models with Non-Gaussian Errors. [Doctoral Dissertation]. University of Florida; 2015. Available from: https://ufdc.ufl.edu/UFE0049518


The Ohio State University

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Olsen, Andrew Nolan. “When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.” 2015. Doctoral Dissertation, The Ohio State University. Accessed January 17, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406.

MLA Handbook (7th Edition):

Olsen, Andrew Nolan. “When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods.” 2015. Web. 17 Jan 2021.

Vancouver:

Olsen AN. When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2021 Jan 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406.

Council of Science Editors:

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

5. Riou-Durand, Lionel. Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique.

Degree: Docteur es, Mathématiques appliquées, 2019, Université Paris-Saclay (ComUE)

La première partie de cette thèse concerne l'inférence de modèles statistiques non normalisés. Nous étudions deux méthodes d'inférence basées sur de l'échantillonnage aléatoire : Monte-Carlo… (more)

Subjects/Keywords: Échantillonnage MCMC; M-Estimateurs; Ergodicité géométrique; Temps de mélange; Couplages; Distance de Wassertein; MCMC sampling; M-Estimators; Geometric ergodicity; Mixing time; Couplings; Wasserstein distance; 510; 65C05; 62; 34A25

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

APA (6th Edition):

Riou-Durand, L. (2019). Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2019SACLG006

Chicago Manual of Style (16th Edition):

Riou-Durand, Lionel. “Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique.” 2019. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed January 17, 2021. http://www.theses.fr/2019SACLG006.

MLA Handbook (7th Edition):

Riou-Durand, Lionel. “Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique.” 2019. Web. 17 Jan 2021.

Vancouver:

Riou-Durand L. Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2019. [cited 2021 Jan 17]. Available from: http://www.theses.fr/2019SACLG006.

Council of Science Editors:

Riou-Durand L. Theoretical contributions to Monte Carlo methods, and applications to Statistics : Contributions théoriques aux méthodes de Monte Carlo, et applications à la Statistique. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2019. Available from: http://www.theses.fr/2019SACLG006


University of Minnesota

6. Johnson, Alicia A. Geometric ergodicity of Gibbs samplers.

Degree: PhD, Statistics, 2009, University of Minnesota

 Due to a demand for reliable methods for exploring intractable probability distributions, the popularity of Markov chain Monte Carlo (MCMC) techniques continues to grow. In… (more)

Subjects/Keywords: Convergence; Drift Conditions; Geometric Ergodicity; Gibbs Samplers; Markov chain Monte Carlo; Statistics

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

APA (6th Edition):

Johnson, A. A. (2009). Geometric ergodicity of Gibbs samplers. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/53661

Chicago Manual of Style (16th Edition):

Johnson, Alicia A. “Geometric ergodicity of Gibbs samplers.” 2009. Doctoral Dissertation, University of Minnesota. Accessed January 17, 2021. http://purl.umn.edu/53661.

MLA Handbook (7th Edition):

Johnson, Alicia A. “Geometric ergodicity of Gibbs samplers.” 2009. Web. 17 Jan 2021.

Vancouver:

Johnson AA. Geometric ergodicity of Gibbs samplers. [Internet] [Doctoral dissertation]. University of Minnesota; 2009. [cited 2021 Jan 17]. Available from: http://purl.umn.edu/53661.

Council of Science Editors:

Johnson AA. Geometric ergodicity of Gibbs samplers. [Doctoral Dissertation]. University of Minnesota; 2009. Available from: http://purl.umn.edu/53661


Loughborough University

7. Zhong, Johnny. Periodic measures, transitions and exit times of stochastic differential equations.

Degree: PhD, 2019, Loughborough University

 Periodic measures are the time-periodic counterpart to invariant measures for dynamical systems that can characterise the long-term periodic behaviour of stochastic dynamical systems. In this… (more)

Subjects/Keywords: Stochastic Differential Equations; Geometric ergodicity; partial differential equation; Feynman-Kac formula; Markov processes.; nonautonomous dynamical system; Stochastic resonance phenomena; expected exit time

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APA (6th Edition):

Zhong, J. (2019). Periodic measures, transitions and exit times of stochastic differential equations. (Doctoral Dissertation). Loughborough University. Retrieved from https://doi.org/10.26174/thesis.lboro.11651226.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.812953

Chicago Manual of Style (16th Edition):

Zhong, Johnny. “Periodic measures, transitions and exit times of stochastic differential equations.” 2019. Doctoral Dissertation, Loughborough University. Accessed January 17, 2021. https://doi.org/10.26174/thesis.lboro.11651226.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.812953.

MLA Handbook (7th Edition):

Zhong, Johnny. “Periodic measures, transitions and exit times of stochastic differential equations.” 2019. Web. 17 Jan 2021.

Vancouver:

Zhong J. Periodic measures, transitions and exit times of stochastic differential equations. [Internet] [Doctoral dissertation]. Loughborough University; 2019. [cited 2021 Jan 17]. Available from: https://doi.org/10.26174/thesis.lboro.11651226.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.812953.

Council of Science Editors:

Zhong J. Periodic measures, transitions and exit times of stochastic differential equations. [Doctoral Dissertation]. Loughborough University; 2019. Available from: https://doi.org/10.26174/thesis.lboro.11651226.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.812953


University of Maryland

8. Li, Ziliang. Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model.

Degree: Mathematical Statistics, 2010, University of Maryland

 In the study of finance, likelihood based or moment based methods are frequently used to estimate parameters for various kinds of models given the sampled… (more)

Subjects/Keywords: Statistics; Economics, Finance; Geometric Ergodicity; Kernel Density Estimate; Method of Moments; Minimum Disparity Estimator; Stochastic Volatility model

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APA (6th Edition):

Li, Z. (2010). Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/10964

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):

Li, Ziliang. “Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model.” 2010. Thesis, University of Maryland. Accessed January 17, 2021. http://hdl.handle.net/1903/10964.

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

MLA Handbook (7th Edition):

Li, Ziliang. “Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model.” 2010. Web. 17 Jan 2021.

Vancouver:

Li Z. Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model. [Internet] [Thesis]. University of Maryland; 2010. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/1903/10964.

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

Council of Science Editors:

Li Z. Minimum Disparity Estimator in Continuous Time Stochastic Volatility Model. [Thesis]. University of Maryland; 2010. Available from: http://hdl.handle.net/1903/10964

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


University of Florida

9. Tan, Aixin. Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models.

Degree: PhD, Statistics, 2009, University of Florida

 Markov chain Monte Carlo (MCMC) methods have received considerable attention as powerful computing tools in Bayesian statistical analysis. The idea is to produce Markov chain… (more)

Subjects/Keywords: Consistent estimators; Ergodic theory; Estimators; Markov chains; Perceptron convergence procedure; Simulations; Standard error; Statistics; Sufficient conditions; Tours; asymptotic, convergence, drift, ergodicity, geometric, minorization, variance

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APA (6th Edition):

Tan, A. (2009). Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0024910

Chicago Manual of Style (16th Edition):

Tan, Aixin. “Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models.” 2009. Doctoral Dissertation, University of Florida. Accessed January 17, 2021. https://ufdc.ufl.edu/UFE0024910.

MLA Handbook (7th Edition):

Tan, Aixin. “Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models.” 2009. Web. 17 Jan 2021.

Vancouver:

Tan A. Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models. [Internet] [Doctoral dissertation]. University of Florida; 2009. [cited 2021 Jan 17]. Available from: https://ufdc.ufl.edu/UFE0024910.

Council of Science Editors:

Tan A. Convergence Rates and Regeneration of the Block Gibbs Sampler for Bayesian Random Effects Models. [Doctoral Dissertation]. University of Florida; 2009. Available from: https://ufdc.ufl.edu/UFE0024910


Virginia Tech

10. Sun, Peng. Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models.

Degree: PhD, Statistics, 2016, Virginia Tech

Subjects/Keywords: Additive Model; Bayes factor; Cubic Splines; Dual-Semiparametric Regression; Generalized Polya urn; Geometric ergodicity; Gibbs sampling; Metropolis-Hastings; Nonparametric Bayesian Model; Ordinal data; Parameterization; Semiparametric Regr

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APA (6th Edition):

Sun, P. (2016). Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78189

Chicago Manual of Style (16th Edition):

Sun, Peng. “Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 17, 2021. http://hdl.handle.net/10919/78189.

MLA Handbook (7th Edition):

Sun, Peng. “Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models.” 2016. Web. 17 Jan 2021.

Vancouver:

Sun P. Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/10919/78189.

Council of Science Editors:

Sun P. Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/78189

.