Download or read book On the Convergence Rate of Markov Chain Monte Carlo Through Mean Commute Time written by 吳其豪 and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Consistency and Convergence Rate of Markov Chain Quasi Monte Carlo with Examples written by Su Chen and published by Stanford University. This book was released on 2011 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Chain Monte Carlo methods have been widely used in various scientific disciplines for generation of samples from distributions that are difficult to simulate directly. The random numbers driving Markov Chain Monte Carlo algorithms are modeled as independent $\mathcal{U}[0,1)$ random variables. The class of distributions that could be simulated are largely broadened by using Markov Chain Monte Carlo. Quasi-Monte Carlo, on the other hand, aims to improve the accuracy of estimation of an integral over the multidimensional unit cube. By using more carefully balanced inputs, under some smoothness conditions the estimation error is converging at a higher rate than plain Monte Carlo. We would like to combine these two techniques, so that we can sample more accurately from a larger class of distributions. This method, called Markov Chain quasi-Monte Carlo (MCQMC), is the main topic of this work. We are going to replace the IID driving sequence used in MCMC algorithms by a deterministic sequence which is designed to be more uniform. Previously the justification for MCQMC is proved only for finite state space case. We are going to extend those results to some Markov Chains on continuous state spaces. We also explore the convergence rate of MCQMC under stronger assumptions. Lastly we present some numerical results for demonstration of MCQMC's performance. From these examples, the empirical benefits of more balanced sequences are significant.
Download or read book Consistency and Convergence Rate of Markov Chain Quasi Monte Carlo with Examples written by Su Chen and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Chain Monte Carlo methods have been widely used in various scientific disciplines for generation of samples from distributions that are difficult to simulate directly. The random numbers driving Markov Chain Monte Carlo algorithms are modeled as independent $\mathcal{U}[0,1)$ random variables. The class of distributions that could be simulated are largely broadened by using Markov Chain Monte Carlo. Quasi-Monte Carlo, on the other hand, aims to improve the accuracy of estimation of an integral over the multidimensional unit cube. By using more carefully balanced inputs, under some smoothness conditions the estimation error is converging at a higher rate than plain Monte Carlo. We would like to combine these two techniques, so that we can sample more accurately from a larger class of distributions. This method, called Markov Chain quasi-Monte Carlo (MCQMC), is the main topic of this work. We are going to replace the IID driving sequence used in MCMC algorithms by a deterministic sequence which is designed to be more uniform. Previously the justification for MCQMC is proved only for finite state space case. We are going to extend those results to some Markov Chains on continuous state spaces. We also explore the convergence rate of MCQMC under stronger assumptions. Lastly we present some numerical results for demonstration of MCQMC's performance. From these examples, the empirical benefits of more balanced sequences are significant.
Download or read book Markov Chain Monte Carlo written by Dani Gamerman and published by CRC Press. This book was released on 2006-05-10 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simul
Download or read book A Simulation Approach to Convergence Rates for Markov Chain Monte Carlo Algorithms written by Mary Kathryn Cowles and published by . This book was released on 1996 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advanced Markov Chain Monte Carlo Methods written by Faming Liang and published by John Wiley & Sons. This book was released on 2011-07-05 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics. Key Features: Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems. A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants. Up-to-date accounts of recent developments of the Gibbs sampler. Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals. This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.
Download or read book Markov Chain Monte Carlo Simulations and Their Statistical Analysis written by Bernd A Berg and published by World Scientific Publishing Company. This book was released on 2004-10-01 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book teaches modern Markov chain Monte Carlo (MC) simulation techniques step by step. The material should be accessible to advanced undergraduate students and is suitable for a course. It ranges from elementary statistics concepts (the theory behind MC simulations), through conventional Metropolis and heat bath algorithms, autocorrelations and the analysis of the performance of MC algorithms, to advanced topics including the multicanonical approach, cluster algorithms and parallel computing. Therefore, it is also of interest to researchers in the field. The book relates the theory directly to Web-based computer code. This allows readers to get quickly started with their own simulations and to verify many numerical examples easily. The present code is in Fortran 77, for which compilers are freely available. The principles taught are important for users of other programming languages, like C or C++.
Download or read book Handbook of Markov Chain Monte Carlo written by Steve Brooks and published by CRC Press. This book was released on 2011-05-10 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie
Download or read book Convergence Rates and Monte Carlo Standard Errors for Markov Chain Monte Carlo Algorithms written by Galin L. Jones and published by . This book was released on 2001 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Markov Chains written by Randal Douc and published by Springer. This book was released on 2019-01-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the classical theory of Markov chains on general state-spaces as well as many recent developments. The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is self-contained, while all the results are carefully and concisely proven. Bibliographical notes are added at the end of each chapter to provide an overview of the literature. Part I lays the foundations of the theory of Markov chain on general states-space. Part II covers the basic theory of irreducible Markov chains on general states-space, relying heavily on regeneration techniques. These two parts can serve as a text on general state-space applied Markov chain theory. Although the choice of topics is quite different from what is usually covered, where most of the emphasis is put on countable state space, a graduate student should be able to read almost all these developments without any mathematical background deeper than that needed to study countable state space (very little measure theory is required). Part III covers advanced topics on the theory of irreducible Markov chains. The emphasis is on geometric and subgeometric convergence rates and also on computable bounds. Some results appeared for a first time in a book and others are original. Part IV are selected topics on Markov chains, covering mostly hot recent developments.
Download or read book Markov Chain Monte Carlo Simulations and Their Statistical Analysis written by Bernd A. Berg and published by World Scientific. This book was released on 2004 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book teaches modern Markov chain Monte Carlo (MC) simulation techniques step by step. The material should be accessible to advanced undergraduate students and is suitable for a course. It ranges from elementary statistics concepts (the theory behind MC simulations), through conventional Metropolis and heat bath algorithms, autocorrelations and the analysis of the performance of MC algorithms, to advanced topics including the multicanonical approach, cluster algorithms and parallel computing. Therefore, it is also of interest to researchers in the field. The book relates the theory directly to Web-based computer code. This allows readers to get quickly started with their own simulations and to verify many numerical examples easily. The present code is in Fortran 77, for which compilers are freely available. The principles taught are important for users of other programming languages, like C or C++.
Download or read book Markov Chain Monte Carlo in Practice written by W.R. Gilks and published by CRC Press. This book was released on 1995-12-01 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France, researchers map a rare disease with relatively little variation. Each of these studies applied Markov chain Monte Carlo methods to produce more accurate and inclusive results. General state-space Markov chain theory has seen several developments that have made it both more accessible and more powerful to the general statistician. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications, providing some theoretical background as well. The authors are researchers who have made key contributions in the recent development of MCMC methodology and its application. Considering the broad audience, the editors emphasize practice rather than theory, keeping the technical content to a minimum. The examples range from the simplest application, Gibbs sampling, to more complex applications. The first chapter contains enough information to allow the reader to start applying MCMC in a basic way. The following chapters cover main issues, important concepts and results, techniques for implementing MCMC, improving its performance, assessing model adequacy, choosing between models, and applications and their domains. Markov Chain Monte Carlo in Practice is a thorough, clear introduction to the methodology and applications of this simple idea with enormous potential. It shows the importance of MCMC in real applications, such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis, and provides an excellent base for MCMC to be applied to other fields as well.
Download or read book Stability of Markov Chain Monte Carlo Methods written by Kengo Kamatani and published by Springer. This book was released on 2015-08-05 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents modern techniques for the analysis of Markov chain Monte Carlo (MCMC) methods. A central focus is the study of the number of iteration of MCMC and the relation to some indices, such as the number of observation, or the number of dimension of the parameter space. The approach in this book is based on the theory of convergence of probability measures for two kinds of randomness: observation randomness and simulation randomness. This method provides in particular the optimal bounds for the random walk Metropolis algorithm and useful asymptotic information on the data augmentation algorithm. Applications are given to the Bayesian mixture model, the cumulative probit model, and to some other categorical models. This approach yields new subjects, such as the degeneracy problem and optimal rate problem of MCMC. Containing asymptotic results of MCMC under a Bayesian statistical point of view, this volume will be useful to practical and theoretical researchers and to graduate students in the field of statistical computing.
Download or read book Convergence Analysis of Markov Chain Monte Carlo Algorithms written by Zhumengmeng Jin and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, we will compare the convergence rate of a two-block Gibbs sampler with an algorithm that reorders the steps in the original Gibbs sampler, which we refer to as the "out-of-order" block Gibbs sampler.
Download or read book Monte Carlo Strategies in Scientific Computing written by Jun S. Liu and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods.
Download or read book Convergence of Adaptive Markov Chain Monte Carlo Algorithms written by Yan Bai and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the thesis, we study ergodicity of adaptive Markov Chain Monte Carlo methods (MCMC) based on two conditions(Diminishing Adaptation and Containment which together imply ergodicity), explain the advantages of adaptive MCMC, and apply the theoretical result for some applications. \indent First we show several facts: 1. Diminishing Adaptation alone may not guarantee ergodicity; 2. Containment is not necessary for ergodicity; 3. under some additional condition, Containment is necessary for ergodicity. Since Diminishing Adaptation is relatively easy to check and Containment is abstract, we focus on the sufficient conditions of Containment. In order to study Containment, we consider the quantitative bounds of the distance between samplers and targets in total variation norm. From early results, the quantitative bounds are connected with nested drift conditions for polynomial rates of convergence. For ergodicity of adaptive MCMC, assuming that all samplers simultaneously satisfy nested polynomial drift conditions, we find that either when the number of nested drift conditions is greater than or equal to two, or when the number of drift conditions with some specific form is one, the adaptive MCMC algorithm is ergodic. For adaptive MCMC algorithm with Markovian adaptation, the algorithm satisfying simultaneous polynomial ergodicity is ergodic without those restrictions. We also discuss some recent results related to this topic. \indent Second we consider ergodicity of certain adaptive Markov Chain Monte Carlo algorithms for multidimensional target distributions, in particular, adaptive Metropolis and adaptive Metropolis-within-Gibbs algorithms. We derive various sufficient conditions to ensure Containment, and connect the convergence rates of algorithms with the tail properties of the corresponding target distributions. We also present a Summable Adaptive Condition which, when satisfied,proves ergodicity more easily. \indent Finally, we propose a simple adaptive Metropolis-within-Gibbs algorithm attempting to study directions on which the Metropolis algorithm can be run flexibly. The algorithm avoids the wasting moves in wrong directions by proposals from the full dimensional adaptive Metropolis algorithm. We also prove its ergodicity, and test it on a Gaussian Needle example and a real-life Case-Cohort study with competing risks. For the Cohort study, we describe an extensive version of Competing Risks Regression model, define censor variables for competing risks, and then apply the algorithm to estimate coefficients based on the posterior distribution.
Download or read book Markov Chain Monte Carlo written by W. S. Kendall and published by World Scientific. This book was released on 2005 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Chain Monte Carlo (MCMC) originated in statistical physics, but has spilled over into various application areas, leading to a corresponding variety of techniques and methods. That variety stimulates new ideas and developments from many different places, and there is much to be gained from cross-fertilization. This book presents five expository essays by leaders in the field, drawing from perspectives in physics, statistics and genetics, and showing how different aspects of MCMC come to the fore in different contexts. The essays derive from tutorial lectures at an interdisciplinary program at the Institute for Mathematical Sciences, Singapore, which exploited the exciting ways in which MCMC spreads across different disciplines.
