Rosenthal, Jeffrey S. - Department of Statistics, University of Toronto

• An Adaptive Directional Metropolis-within-Gibbs algorithm
• Understanding MCMC: Exercise Solutions 1. (a) We compute that xX (1/3) P(x, {y}) = 1/3 for all y X.
• Geometric Ergodicity and Hybrid Markov Chains Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Random Rotations: Characters and Random Walks on SO(N) (Appeared in Annals of Probability 22 (1994), 398--423.)
• 1 Relabeling Algorithm in High Dimension Since the algorithm has been well explained in previous sections and in our
• Markov chain convergence: from finite to infinite Jeffrey S. Rosenthal*
• Examples of Adaptive MCMC Gareth O. Roberts * and Je#rey S. Rosenthal **
• Understanding MCMC: Exercise Solutions 1. (a) We compute that
• Optimal scaling of discrete approximations to Langevin diffusions Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Adaptive Gibbs samplers and related MCMC methods
• When Can Martingales Avoid Ruin? Maury Bramson1
• Asymptotic Variance and Convergence Rates of Nearly-Periodic MCMC Algorithms
• BridgeEntry MainAirlock
• Bounding the convergence time of the Gibbs sampler in Bayesian image restoration
• Optimal scaling of discrete approximations to Langevin diffusions Gareth O. Roberts* and Jeffrey S. Rosenthal**
• ON THE GEOMETRIC ERGODICITY OF HYBRID SAMPLERS G. FORT, E. MOULINES, G. O. ROBERTS, AND J. S. ROSENTHAL
• Small and Pseudo-Small Sets for Markov Chains Gareth O. Roberts* and Je rey S. Rosenthal**
• Combinatorial identities associated with CFTP Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo (September, 1993; revised July, 1994.)
• Norm Comparisons for Data Augmentation James P. Hobert 1 and Je#rey S. Rosenthal 2
• Understanding MCMC, Lancaster 2003 1. Let X = f1; 2; 3g, and consider the Markov chain with transitions P (1; f2g) =
• TOWARDS OPTIMAL SCALING OF METROPOLIS-COUPLED MARKOV CHAIN MONTE CARLO
• Statistical Decision Theory: Concepts, Methods and Applications
• Understanding MCMC, Lancaster 2003 1. Let X = {1, 2, 3}, and consider the Markov chain with transitions P(1, {2}) =
• Finding Generators for Markov Chains via Empirical Transition Matrices,
• bottle,bill,food OrangeWalkN
• Combinatorial identities associated with CFTP Gareth O. Roberts* and Je rey S. Rosenthal**
• Simultaneous drift conditions for Adaptive Markov Chain Monte Carlo algorithms
• Convergence of Independent Particle Systems John R. Hoffman*
• Was the Conservative Majority Predictable? Jeffrey S. Rosenthal*
• Appendix: Selected Answers Section 1.2
• On The Weak Law Of Large Numbers For Unbounded Functionals For Adaptive MCMC
• A simulation approach to convergence rates for Markov chain Monte Carlo algorithms
• f-Uniform Ergodicity of Markov Chains Supervised Project
• A Farewell to Mr.Chretien! ...now it's your turn to fuddle duddle.
• Quantitative convergence rates of Markov chains: A simple account
• Extension of Fill's perfect rejection sampling algorithm to general chains
• On Generalizing the Cutoff Phenomenon for Random Walks on Groups Jeffrey S. Rosenthal
• The Bieberbach Conjecture A minor thesis submitted by
• On Variance Conditions for Markov Chain CLTs Olle Haggstrom * and Je#rey S. Rosenthal **
• Learn From Thy Neighbor: ParallelChain and Regional Adaptive MCMC
• Link Analysis Ranking Algorithms, Theory, and Experiments #
• Les marches aleatoires et les algorithmes MCMC
• STA4000 READING COURSE FINAL REPORT STA4000 READING COURSE--FINAL REPORT
• Markov Chain Monte Carlo Algorithms: Theory and Practice
• Optimal Acceptance Rates for Metropolis Algorithms: Moving Beyond 0.234
• shower,brick OnEnclosure
• A Case Study in the Meta-Reasoning Procedure ND James J. Lu
• An Historical Survey of the Development of Probability and Statistics based on the Appearance of Fundamental Concepts 1
• Random Rotations: Characters and Random Walks on SO(N) (Appeared in Annals of Probability 22 (1994), 398423.)
• Random Walks on Discrete and Continuous Circles Jeffrey S. Rosenthal
• A Random Walk Through the Big Metropolis (Couples Welcome)
• Understanding MCMC, Lancaster 2003 Laboratory exercises
• Markov chain convergence: from finite to infinite Jeffrey S. Rosenthal*
• FrontYard *** UpstairsLand
• 1 Comparison of Adult O ense Prediction Methods Comparison of Adult O ense Prediction Methods
• Random Walks on Discrete and Continuous Circles Je rey S. Rosenthal
• Active-Learning Strategies in Advanced Mathematics Classes Je rey S. Rosenthal
• Adaptive Gibbs samplers Krzysztof Latuszynski and Je#rey S. Rosenthal
• Time to L 2 for certain random walks on compact Lie groups* (Notes in progress, 1994.)
• Convergence de l'echantillonneur de Gibbs pour les distributions uniformes
• Meetings with costly participation By Martin J. Osborne, Jeffrey S. Rosenthal,
• Downweighting Tightly Knit Communities in World Wide Web Rankings
• GreekFrieze RuinedCastle
• Convergence rates of Markov chains (December 1993. Revised, September 1994.)
• PROBABILITY AND MCMC Course notes for STA 4276H, Fall 2003.
• Possible biases induced by MCMC convergence diagnostics Mary Kathryn Cowles*, Gareth O. Roberts** and Jeffrey S. Rosenthal***
• On the Containment Condition for Adaptive Markov Chain Monte Carlo Algorithms
• Convergence of Pseudo-Finite Markov Chains Jeffrey S. Rosenthal
• Asymptotic Variance and Convergence Rates of Nearly-Periodic MCMC Algorithms
• Generalization of Discrete-time Geometric Bounds to Convergence Rate of Markov Processes on R n
• Material on: Reversible Processes Prepared by Martin van Driel
• Errata for FIRST edition of ``A First Look at Rigorous Probability'' NOTE: the corrections below (plus many more improvements) were all encorporated into
• Crossword Puzzles: A Case Study in ComputeIntensive MetaReasoning ?
• Faithful couplings of Markov chains: now equals forever Jeffrey S. Rosenthal*
• Authors' rejoinder to Markov chain Monte Carlo: Some practical implications
• Fields Institute Communications Volume 00, 0000
• STATISTICS AND MCMC Jeffrey S. Rosenthal
• On convergence rates of Gibbs samplers for uniform distributions Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Coupling and Ergodicity of Adaptive MCMC Gareth O. Roberts* and Je#rey S. Rosenthal**
• TallEWCanyon SwissCheese
• Optimal scaling for various Metropolis-Hastings algorithms
• Quantitative convergence rates of Markov chains: A simple account
• A review of asymptotic convergence for general state space Markov chains
• One-Shot Coupling for Certain Stochastic Recursive Sequences
• Convergence of slice sampler Markov chains Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Geometric Ergodicity and Hybrid Markov Chains Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Convergence properties of perturbed Markov chains Gareth O. Roberts*, Jeffrey S. Rosenthal**, and Peter O. Schwartz***
• Analysis of the Gibbs sampler for a model related to James-Stein estimators
• Adaptive Gibbs samplers Krzysztof Latuszynski and Jeffrey S. Rosenthal
• Introduction Since its advent about 15 years ago, the criminology and psychology
• Trajectories of Criminal Activity in a Sample of 378 Adjudicated Ontario Youth David. M. Day, 1
• BAYESIAN STATISTICS 9, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid,
• Adaptation to Climate Change: Evidence from 18th and 19th Century Iceland
• Criminal Activity of Adjudicated Youth Long-term Follow-up of Criminal Activity with Adjudicated Youth in Ontario
• Beyond the Quintessential Quincunx Michael A. Proschan 1
• On the Containment Condition for Adaptive Markov Chain Monte Carlo Algorithms
• On the Containment Condition for Adaptive Markov Chain Monte Carlo Algorithms
• Learn From Thy Neighbor: Parallel-Chain and Regional Adaptive MCMC
• Norm Comparisons for Data Augmentation James P. Hobert1
• Waiting Time Correlations on Disorderly Streetcar Routes
• 1 Comparison of Adult Offense Prediction Methods Comparison of Adult Offense Prediction Methods
• Downweighting Tightly Knit Communities in World Wide Web Rankings
• Achieving Limiting Distributions for Markov Chains Using Back Buttons
• Asymptotic Variance and Convergence Rates of Nearly-Periodic MCMC Algorithms
• A note on geometric ergodicity and floating-point roundoff error
• Finding Generators for Markov Chains via Empirical Transition Matrices,
• Recent progress on computable bounds and the simple slice sampler
• Parallel computing and Monte Carlo algorithms Jeffrey S. Rosenthal*
• Moment conditions for a sequence with negative drift to be uniformly bounded in Lr
• An extension of Fill's exact sampling algorithm to non-monotone chains*
• A note on convergence rates of Gibbs sampling for nonparametric mixtures
• Possible biases induced by MCMC convergence diagnostics Mary Kathryn Cowles*, Gareth O. Roberts** and Jeffrey S. Rosenthal***
• On convergence rates of Gibbs samplers for uniform distributions Gareth O. Roberts* and Jeffrey S. Rosenthal**
• STATISTICS AND MCMC Jeffrey S. Rosenthal
• Two convergence properties of hybrid samplers Gareth O. Roberts* and Jeffrey S. Rosenthal**
• E l e c t r o n i o b a b i l i t y
• Faithful couplings of Markov chains: now equals forever Jeffrey S. Rosenthal*
• Shift-coupling and convergence rates of ergodic averages
• RATES OF CONVERGENCE FOR EVERYWHERE-POSITIVE MARKOV CHAINS
• Supplement to "Minorization conditions and convergence rates for Markov chain Monte Carlo"
• Rates of Convergence for Data Augmentation on Finite Sample Spaces Jeffrey S. Rosenthal
• CHAPTER ONE. Introduction. My Ph.D. thesis is concerned with convergence properties of certain Markov
• CHAPTER SIX. Concluding Comments. In this thesis, we have considered various Markov chains, and in particular their
• Theoretical rates of convergence for Markov chain Monte Carlo
• Active-Learning Strategies in Advanced Mathematics Classes Jeffrey S. Rosenthal
• The Bieberbach Conjecture A minor thesis submitted by
• for certain random walks on compact Lie groups* (Notes in progress, 1994.)
• Markov chain Monte Carlo Gareth O. Roberts1
• Errata for the SECOND EDITION of "A First Look at Rigorous Probability", by Jeffrey S. Rosenthal, World Scientific Publishing Co., 2006.
• Errata for FIRST edition of "A First Look at Rigorous Probability" NOTE: the corrections below (plus many more improvements) were all encorporated into
• Science Center 7th Fl. One Oxford Street
• How to Discuss Statistics on Live Television Jeffrey S. Rosenthal, University of Toronto
• Pickard Memorial Symposium Musical Program November 13, 2010
• Ergodicity of Adaptive MCMC and its Applications A thesis submitted in conformity with the requirements
• Recurrent and Ergodic Properties of Adaptive MCMC Department of Mathematics,
• Weak Convergence of Metropolis Algorithms for Non-iid Target Distributions
• Source Codes for Various Simulation Studies in Chapter 4
• STA4000 Report Decrypting Classical Cipher Text
• Convergence of Adaptive Markov Chain Monte Carlo Algorithms
• Label Switch in Mixture Model and Relabeling Algorithm Project for Reading Course
• A Model-Based Indicator of Musculoskeletal Disorders
• of Liber De Ludo Aleae (Book on Games of Chance) by Gerolamo Cardano 1. Biographical Notes Concerning Cardano
• Prof. G.O. ROBERTS: PUBLICATIONS August 2008 Refereed publications
• GreekFrieze RuinedCastle
• Optimising Monte Carlo Search Strategies for Automated Pattern Detection
• Optimal Proposal Distributions and Adaptive MCMC
• Convergence Rate of Markov Chain Methods for Genomic Motif Discovery
• Linear-Algebraic Results Associated with Antiferromagnetic Heisenberg Chains*
• CRIMINAL TRAJECTORIES FROM ADOLESCENCE TO ADULTHOOD IN AN ONTARIO SAMPLE OF OFFENDERS
• EXTREMAL INDICES, GEOMETRIC ERGODICITY OF MARKOV CHAINS, AND MCMC
• Decrypting Classical Cipher Text Using Markov Chain Monte Carlo
• GENERAL STATE SPACE MARKOV CHAINS AND MCMC ALGORITHMS
• Theoretical rates of convergence for Markov chain Monte Carlo
• TOWARDS OPTIMAL SCALING OF METROPOLISCOUPLED MARKOV CHAIN MONTE CARLO
• Efficient Use of Exact Samples Duncan J. Murdoch* and Jeffrey S. Rosenthal**
• Variance Bounding Markov Chains Gareth O. Roberts * and Je#rey S. Rosenthal **
• Finding Authorities and Hubs From Link Structures on the World Wide Web
• Optimal Proposal Distributions and Adaptive MCMC
• Parallel computing and Monte Carlo algorithms Je rey S. Rosenthal*
• On the Containment Condition for Adaptive Markov Chain Monte Carlo Algorithms
• NATIONAL BESTSELLING AUTHOR OF STRUCK BY LIGHTNING
• Efficient Use of Exact Samples Duncan J. Murdoch* and Jeffrey S. Rosenthal**
• Authors' rejoinder to Markov chain Monte Carlo: Some practical implications
• CONVERGENCE OF ADAPTIVE MARKOV CHAIN MONTE CARLO ALGORITHMS
• The Annals of Applied Probability 2008, Vol. 18, No. 3, 12011214
• Stochastic Justification of Some Simple Reliability Thierry Duchesne and Jeffrey S. Rosenthal
• STA4000 Final Report -Summary Investigation on Iceland Population Growth and Climate
• Change and Continuity in Criminal Offending: Criminal Trajectories of the "Toronto" Sample
• In nite hierarchies and prior distributions Gareth O. Roberts* and Je rey S. Rosenthal**
• B. WEB APPENDIX: PROOF OF THEOREMS 3.2-3.4 AND B.1 Proof of Theorem 3.2
• Criminal Trajectories and Risk Factors in a Canadian Sample of Offenders Criminal Trajectories and Risk Factors in a Canadian Sample of Offenders
• Quantitative Non-Geometric Convergence Bounds
• APPLICATIONS OF GEOMETRIC BOUNDS TO THE CONVERGENCE RATE OF MARKOV CHAINS ON R n
• Achieving Limiting Distributions for Markov Chains Using Back Buttons
• RATES OF CONVERGENCE FOR EVERYWHEREPOSITIVE MARKOV CHAINS
• Markov chain Monte Carlo: Some practical implications of theoretical results
• Optimising and Adapting the Metropolis Algorithm
• GENERAL STATE SPACE MARKOV CHAINS AND MCMC ALGORITHMS
• Detecting Multiple Authorship of United States Supreme Court Legal Decisions Using Function Words
• Quantitative NonGeometric Convergence Bounds
• Waiting Time Correlations on Disorderly Streetcar Routes
• AMCMC: An R interface for adaptive MCMC
• Convergence of Independent Particle Systems John R. Hoffman*
• An Adaptive Metropolis algorithm Heikki Haario, Eero Saksman \Lambda
• Markov Chain Monte Carlo Algorithms: Theory and Practice
• UNDERSTANDING MCMC Gareth O. Roberts and Jeffrey S. Rosenthal
• STA4000 Report Decrypting Classical Cipher Text
• Recent progress on computable bounds and the simple slice sampler
• Examples of Adaptive MCMC Gareth O. Roberts*
• Understanding MCMC, Lancaster 2003 Laboratory exercises
• The Polar Slice Sampler Gareth O. Roberts* and Je rey S. Rosenthal**
• An Introduction to Markov Chain Monte Carlo
• Markov chain Monte Carlo Gareth O. Roberts 1 and Je rey S. Rosenthal 2
• On Duality of Probabilities for Carddealing Jeffrey S. Rosenthal
• Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo (September, 1993; revised July, 1994.)
• Infinite hierarchies and prior distributions Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Markov chain Monte Carlo: Some practical implications of theoretical results
• On Duality of Probabilities for Card-dealing Jeffrey S. Rosenthal
• Adaptive MCMC: Background, Theory, and Simulations
• The Annals of Applied Statistics 2011, Vol. 5, No. 1, 283308
• A Cancellativity Criterion for a Class of Spin Systems
• Shift-coupling and convergence rates of ergodic averages
• CONVERGENCE IN THE WASSERSTEIN METRIC FOR MARKOV CHAIN MONTE CARLO
• Moment conditions for a sequence with negative drift to be uniformly bounded in L r
• Markov Chains Convergence Supervised Project
• Prof. Derek Bingham Department of Mathematics
• Message from the President
• Two convergence properties of hybrid samplers Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Efficient Use of Exact Samples Duncan Murdoch
• A simulation approach to convergence rates for Markov chain Monte Carlo algorithms
• Harris Recurrence of Metropolis-Within-Gibbs and Trans-Dimensional Markov Chains
• Expressions for the Markov Chain CLT Variance Je#rey S. Rosenthal *
• ON CHOOSING AND BOUNDING PROBABILITY ALISON L. GIBBS AND FRANCIS EDWARD SU
• Notes About Markov Chain CLTs [Rough notes by Jeffrey S. Rosenthal, February 2007, based on very helpful conversations
• On Variance Conditions for Markov Chain CLTs Olle Haggstrom*
• Markov Chains Monte Carlo Algorithms University of Toronto
• Expressions for the Markov Chain CLT Variance Jeffrey S. Rosenthal*
• Convergence rates of Markov chains (December 1993. Revised, September 1994.)
• Extension of Fill's perfect rejection sampling algorithm to general chains
• STA4000 Final Report -Detailed Analysis Investigation on Iceland Population Growth and Climate
• A Case Study in the MetaReasoning Procedure ND James J. Lu y Jeffrey S. Rosenthal z Andrew E. Shaffer x
• Supplement to ``Minorization conditions and convergence rates for Markov chain Monte Carlo''
• LinearAlgebraic Results Associated with Antiferromagnetic Heisenberg Chains*
• Prof. G.O. ROBERTS: PUBLICATIONS August 2008 Refereed publications
• Shiftcoupling and convergence rates of ergodic averages Gareth O. Roberts* and Jeffrey S. Rosenthal**
• E l e c t r o n i c r o b a b i l i t y
• Perfect Forward Simulation via Simulated Tempering Stephen P. Brooksy
• On the Collapsibility of Lifetime Regression Models Thierry Duchesne
• Geometric Convergence Rates for Time-Sampled Markov Chains Je rey S. Rosenthal*
• Adaptive Gibbs samplers and related MCMC methods
• Markov Chains Convergence Supervised Project
• Rates of Convergence for Gibbs Sampling for Variance Component Models Jeffrey S. Rosenthal
• Monty Hall, Monty Fall, Monty Crawl Jeffrey S. Rosenthal
• AMCMC: An R interface for adaptive MCMC
• arXiv:0801.1864v1[stat.CO]11Jan2008 Adaptive Independent Metropolis-Hastings by Fast
• Analysis of the Gibbs sampler for a model related to JamesStein estimators
• A mathematical analysis of the Sleeping Beauty problem
• Phone: 336-758-5354 Fax: 336-758-7190
• Geometric Convergence Rates for Time-Sampled Markov Chains Jeffrey S. Rosenthal*
• RATES OF CONVERGENCE FOR GIBBS SAMPLER AND OTHER MARKOV CHAINS
• Convergence rates of Markov chain Monte Carlo methods by: David Clement*
• EXTREMAL INDICES, GEOMETRIC ERGODICITY OF MARKOV CHAINS, AND MCMC
• Asymptotic Variance and Convergence Rates of Nearly-Periodic MCMC Algorithms
• A note on convergence rates of Gibbs sampling for nonparametric mixtures \Lambda
• Markov Chains and De-initialising Processes Gareth O. Roberts* and Je rey S. Rosenthal**
• A review of asymptotic convergence for general state space Markov chains
• Scaling Limits for the Transient Phase of Local Metropolis-Hastings Algorithms
• Rates of Convergence for Data Augmentation on Finite Sample Spaces Je rey S. Rosenthal
• A note on geometric ergodicity and oating-point roundo error
• On Adaptive Markov Chain Monte Carlo Algorithms Yves F. Atchad 1 and Je rey S. Rosenthal 2
• Recent progress on computable bounds and the simple slice sampler
• Gambling Systems and MultiplicationInvariant Measures Jeffrey S. Rosenthal* and Peter O. Schwartz**
• Detecting Multiple Authorship of United States Supreme Court Legal Decisions Using Function Words
• Variance Bounding Markov Chains Gareth O. Roberts*
• Efficient Sampling Using Metropolis Algorithms: Applications of Optimal Scaling Results
• Coupling and Ergodicity of Adaptive MCMC Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Fields Institute Communications Volume 00, 0000
• An extension of Fill's exact sampling algorithm to nonmonotone chains*
• Convergence properties of perturbed Markov chains Gareth O. Roberts*, Jeffrey S. Rosenthal**, and Peter O. Schwartz***
• Errata for the SECOND EDITION of ``A First Look at Rigorous Probability'', by Je#rey S. Rosenthal, World Scientific Publishing Co., 2006.
• When Can Martingales Avoid Ruin? Maury Bramson 1 , Jeremy Quastel 2 , Jeffrey S. Rosenthal 2
• STATISTICS AND MCMC Jeffrey S. Rosenthal
• CONVERGENCE OF MARKOV CHAIN MONTE CARLO ALGORITHMS WITH APPLICATIONS TO
• Rates of Convergence for Gibbs Sampling for Variance Component Models Je rey S. Rosenthal
• The Polar Slice Sampler Gareth O. Roberts* and Jeffrey S. Rosenthal**
• An Introduction to Markov Chain Monte Carlo
• Harris Recurrence of Metropolis-Within-Gibbs and Trans-Dimensional Markov Chains
• Rosenthal-Yoon Judicial Authorship: please do not cite or quote Judicial Ghostwriting: Authorship on the U.S. Supreme Court
• Decrypting Classical Cipher Text Using Markov Chain Monte Carlo
• UNIVERSIT DES ANTILLES ET DE LA GUYANE pour obtenir le grade de
• Markov Chains and De-initialising Processes Gareth O. Roberts* and Jeffrey S. Rosenthal**
• Was the Conservative Majority Predictable? Je#rey S. Rosenthal *
• Optimising Monte Carlo Search Strategies for Automated Pattern Detection
• Convergence of PseudoFinite Markov Chains Jeffrey S. Rosenthal
• Strategies for Using the Back Button when Browsing the Web
• A mathematical analysis of the Sleeping Beauty problem
• Des Resultats theoriques sur les algorithmes Monte Carlo par cha^ines de Markov
• Gambling Systems and Multiplication-Invariant Measures Jeffrey S. Rosenthal* and Peter O. Schwartz**
• Finding Authorities and Hubs From Link Structures on the World Wide Web
• A regeneration proof of the central limit theorem for uniformly ergodic Markov chains
• Scaling Limits for the Transient Phase of Local Metropolis-Hastings Algorithms
• On Generalizing the Cut-off Phenomenon for Random Walks on Groups Jeffrey S. Rosenthal
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• Journal of Multivariate Analysis Optimal Spherical Deconvolution1
• Servant dictionary battery, map
• HollowSpace stove,fridge
• Application of Geometric Bounds to Convergence Rates of Markov Chains and
• Convergence of slice sampler Markov chains Gareth O. Roberts* and Jeffrey S. Rosenthal**
• CommonRoom *** disk Entryway BigBedroom [1]
• One-Shot Coupling for Certain Stochastic Recursive Sequences
• A note on geometric ergodicity and oating-point roundo error
• Convergence de l' echantillonneur de Gibbs pour les distributions uniformes
• On the Ergodicity Properties of some Adaptive MCMC Christophe Andrieu
• A note on geometric ergodicity and floating-point roundoff error
• BioMed Central Page 1 of 10
• Group-based Criminal Trajectory Analysis using Cross-Validation Criteria
• Some Results on the Ergodicity of Adaptive MCMC Algorithms
• AppendixAppendixAppendixAppendix 1.1.1.1. CodeCodeCodeCode
• LocalLocalLocalLocal scalescalescalescale adaptationadaptationadaptationadaptation forforforfor RandomRandomRandomRandom WalkWalkWalkWalk MetropolisMetropolisMetropolisMetropolis
• An Adaptive Markov Chain Monte Carlo Algorithm-Regime Change Algorithm
• Groupbased Criminal Trajectory Analysis using CrossValidation Criteria
• Weak Convergence and Optimal Proposals for Monte Carlo Algorithms Under the supervision of Professor J.S. Rosenthal
• The Mathematics of Your Next Family Reunion Jeffrey S. Rosenthal
• ISBA Bulletin, 18(1), March 2011 INTERVIEWS JEFF ROSENTHAL
• IKANOCRACY: GOVERNMENT BY THE COMPETENT G. MACDONALD
• Stat Comput (2012) 22:397413 DOI 10.1007/s11222-011-9232-5
• Adaptation to Climate Change in Preindustrial Iceland By Matthew A. Turner, Jeffrey S. Rosenthal, Jian Chen, and Chunyan Hao
• Trace Plots for Theta, Mu, Sigma for Metropolis within Gibbs applied to Discrete Time Model Trace Plots for Theta, Mu, Sigma for Metropolis within Gibbs applied to Continuous Time Model
• Criminal Activity of Adjudicated Youth Long-term follow-up of criminal activity with adjudicated youth in Ontario: Identifying offence
• INFERENCE ON SPATIAL INFECTIOUS-DISEASE MODELS WITH PLANTATION DATA