Lopes Hedibert F (30 results)

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Condition: New. Dani Gamerman: Ph. D. in Statistics from University of Warwick in 1987. Professor of Statistics at UFRJ from 1996 to 2019. Professor Emeritus at UFRJ since 2021. Supervises graduate students and post-doctoral researchers. Author of the .

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Taschenbuch. Condition: Neu. Neuware - Marking a pivotal moment in the evolution of Bayesian inference, this third edition of this seminal textbook on Markov Chain Monte Carlo (MCMC) methods reflects the profound transformations in both the fields of statistics and the broader landscape of data science over the past two decades. Building on the foundations laid by its first two editions, this updated volume, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Third Edition, addresses the challenges posed by modern datasets, which now span millions or even billions of observations and high-dimensional parameter spaces. While faster, approximate methods have gained traction, MCMC remains the gold standard for rigorous and reliable Bayesian inference, and this book continues to champion its relevance in the face of evolving computational paradigms.This latest edition introduces significant updates and expansions, including new material on infinite-dimensional MCMC, sequential Monte Carlo methods, and adaptive algorithms. It also revisits foundational topics with fresh insights, such as iterative dynamics, mixture distributions, and data augmentation, while incorporating cutting-edge developments like Hamiltonian Monte Carlo (HMC) and Dirichlet process-based methods. With a focus on both theoretical rigor and practical application, the book equips readers to navigate the complexities of modern Bayesian modeling and computation.Features - Expanded coverage of sequential Monte Carlo methods, complementing MCMC with probabilistic foundations - A brand-new chapter on infinite-dimensional MCMC, addressing advanced stochastic simulation techniques for modern Bayesian modeling - Enhanced theoretical treatment of Markov chains on continuous state spaces, including nonhomogeneous Markov chains and adaptive algorithms - New sections on mixture distributions and data augmentation, showcasing their power in simplifying and improving MCMC algorithms - Detailed exploration of HMC and Dirichlet process-based methods, reflecting recent advances in high-dimensional and scalable MCMC techniques - Completely revised software section, aligning with contemporary Bayesian computation practices and tools, with accompanying R and Python codes available on GitHub This textbook is an essential resource for statisticians, data scientists, and researchers in fields such as machine learning, artificial intelligence, and computational biology, who rely on Bayesian inference for analyzing complex, high-dimensional datasets. It is equally valuable for graduate students and academics seeking a comprehensive introduction to MCMC methods, as well as practitioners looking to deepen their understanding of modern Bayesian computation. With its blend of theoretical depth and practical guidance, this third edition serves as both a foundational text and a reference for advanced applications in the ever-expanding domain of Bayesian analysis.…

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Hardback. Condition: New. 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 Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration.Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.…

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Hardcover. Condition: Brand New. 2nd edition. 323 pages. 9.25x6.25x0.75 inches. In Stock.

Condition: New. Dani Gamerman: Ph. D. in Statistics from University of Warwick in 1987. Professor of Statistics at UFRJ from 1996 to 2019. Professor Emeritus at UFRJ since 2021. Supervises graduate students and post-doctoral researchers. Author of the .

Condition: New.

Hardback. Condition: New. 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 Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration.Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.…

Buch. Condition: Neu. Neuware - Marking a pivotal moment in the evolution of Bayesian inference, this third edition of this seminal textbook on Markov Chain Monte Carlo (MCMC) methods reflects the profound transformations in both the fields of statistics and the broader landscape of data science over the past two decades. Building on the foundations laid by its first two editions, this updated volume, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Third Edition, addresses the challenges posed by modern datasets, which now span millions or even billions of observations and high-dimensional parameter spaces. While faster, approximate methods have gained traction, MCMC remains the gold standard for rigorous and reliable Bayesian inference, and this book continues to champion its relevance in the face of evolving computational paradigms.This latest edition introduces significant updates and expansions, including new material on infinite-dimensional MCMC, sequential Monte Carlo methods, and adaptive algorithms. It also revisits foundational topics with fresh insights, such as iterative dynamics, mixture distributions, and data augmentation, while incorporating cutting-edge developments like Hamiltonian Monte Carlo (HMC) and Dirichlet process-based methods. With a focus on both theoretical rigor and practical application, the book equips readers to navigate the complexities of modern Bayesian modeling and computation.Features - Expanded coverage of sequential Monte Carlo methods, complementing MCMC with probabilistic foundations - A brand-new chapter on infinite-dimensional MCMC, addressing advanced stochastic simulation techniques for modern Bayesian modeling - Enhanced theoretical treatment of Markov chains on continuous state spaces, including nonhomogeneous Markov chains and adaptive algorithms - New sections on mixture distributions and data augmentation, showcasing their power in simplifying and improving MCMC algorithms - Detailed exploration of HMC and Dirichlet process-based methods, reflecting recent advances in high-dimensional and scalable MCMC techniques - Completely revised software section, aligning with contemporary Bayesian computation practices and tools, with accompanying R and Python codes available on GitHub This textbook is an essential resource for statisticians, data scientists, and researchers in fields such as machine learning, artificial intelligence, and computational biology, who rely on Bayesian inference for analyzing complex, high-dimensional datasets. It is equally valuable for graduate students and academics seeking a comprehensive introduction to MCMC methods, as well as practitioners looking to deepen their understanding of modern Bayesian computation. With its blend of theoretical depth and practical guidance, this third edition serves as both a foundational text and a reference for advanced applications in the ever-expanding domain of Bayesian analysis.…

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dani Gamerman, Hedibert F. LopesWhile 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 b.…