This thoroughly revised and expanded second edition of the Handbook of Markov Chain Monte Carlo reflects the dramatic evolution of MCMC methods since the publication of the first edition. With the addition of two new editors, Radu V. Craiu and Dootika Vats, this comprehensive reference now offers deeper insights into the theoretical foundations and cutting-edge developments that are reshaping the field.
Features:
- Completely restructured content with 13 updated chapters from the first edition and 10 entirely new chapters reflecting the latest methodological advances
- In-depth coverage of recent breakthroughs in multi-modal sampling, intractable likelihood problems, and involutive MCMC theory
- Comprehensive exploration of unbiased MCMC methods, control variates, and rigorous convergence bounds
- Practical guidance on implementing MCMC algorithms on modern hardware and software platforms
- Cutting-edge material on the integration of MCMC with deep learning and other machine learning approaches
- Authoritative treatment of theoretical foundations alongside practical implementation strategies
This essential reference serves statisticians, computer scientists, physicists, data scientists, and researchers across disciplines who employ computational methods for Bayesian inference and stochastic simulation. Graduate students will find it an invaluable learning resource, while experienced practitioners will appreciate its balance of theoretical depth and practical implementation advice. Whether used as a comprehensive guide to current MCMC methodology or as a reference for specific advanced techniques, this handbook provides the definitive resource for anyone working at the intersection of Bayesian computation and modern statistical modeling.
With the addition of two new editors, Radu V. Craiu and Dootika Vats, this thoroughly revised and expanded second edition of the Handbook of Markov Chain Monte Carlo reflects the dramatic evolution of MCMC methods since the publication of the first edition,
1. Introduction to MCMC
2. MCMC using Hamiltonian Dynamics
3. Optimising
and Adapting Metropolis Algorithm Proposal Distributions
4. How Many
Iterations to Run?
5. Implementing MCMC: Multivariate Estimation with
Confidence
6. Importance Sampling, Simulated Tempering, and Umbrella Sampling
7. Reversible Jump MCMC
8. Perfecting MCMC Sampling: Recipes and Reservations
9. The Data Augmentation Algorithm
10. Latent Gaussian Models and Computation
for Large Spatial Data
11. Efficient MCMC in Astronomy
12. Computationally
Intensive Inverse Problems
13. MCMC for State Space Models
14. MCMC Methods
for Multi-modal Distributions
15. Algorithms for Models with Intractable
Normalizing Functions
16. Involutive theory of MCMC
17. Unbiased MCMC
18.
Control Variates for MCMC
19. Convergence Bounds for MCMC
20. Perturbations
of Markov Chains
21. Running MCMC on Modern Hardware and Software
22.
Bayesian Computation in Deep Learning
23. MCMC-driven Learning
Radu V. Craiu is a professor of statistics at the University of Toronto. His research interests are in computational methods in statistics, statistical inference, copula models, model selection procedures, and the use of statistical methods for scientific advancement in genetics, astronomy and demography. He is currently Contributing Editor for the IMS Bulletin and Associate Editor for the Harvard Data Science Review, Journal of Computational and Graphical Statistics, Statistics Surveys, The Canadian Journal of Statistics, and Statistical Methods and Applications. He received the CRM-SSC prize, is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, a Faculty Affiliate of the Vector Institute, and an Elected Member of the International Statistical Institute.
Dootika Vats is an associate professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur, India. Her research interests include output analysis for stochastic simulation, Markov chain Monte Carlo methods, proximal methods in Bayesian computation, and stochastic optimization. In 2021, she was one of the winners of the Blackwell-Rosenbluth Award given by the junior-International Society for Bayesian Analysis. She currently serves as an Associate Editor for Bayesian Analysis, Journal of Computational and Graphical Statistics, and Sankhya B.
Galin L. Jones is Lynn Y. S. Lin Professor of Statistics and Director of the School of Statistics at the University of Minnesota. His primary research interests include Markov chain Monte Carlo, statistical theory and methods in both Bayesian and frequentist domains, as well as applications in neuroimaging and the physical sciences. He has collaborated with a wide range of researchers, including psychologists, veterinarians, librarians, ecologists, and astrophysicists, among others. Jones is an elected fellow of both the American Statistical Association and the Institute for Mathematical Statistics and is past Co-Editor of the Journal of Computational and Graphical Statistics.
Steve Brooks is director and founder of Select Statistics, a statistical consultancy business based in the United Kingdom. He was formerly professor of Statistics at Cambridge University and received the Royal Statistical Society Guy medal in Bronze in 2005 and the Philip Leverhulme prize in 2004. Like his co-editors, he has served on numerous professional committees both in the United Kingdom and elsewhere, as well as sitting on numerous editorial boards. He is co-author of Bayesian Analysis for Population Ecology (Chapman & Hall/CRC, 2009) and co-founder of the National Centre for Statistical Ecology. His research interests include the development and application of computational statistical methodology across a broad range of application areas.
Andrew Gelman is a professor of statistics and political science at Columbia University. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), Regression and Other Stories (with Jennifer Hill and Aki Vehtari), Active Statistics (with Aki Vehatri), and the forthcoming BayesianWorkflow (with many collaborators). He has done research on applications ranging from elections and public opinion to laboratory assays and toxicology; on the theory and practice of Bayesian statistical methods, from design and data collection through modeling, analysis, and model evaluation; and on statistical computing, graphics, and communication.
Xiao-Li Meng is the Whipple V. N. Jones Professor of Statistics at Harvard, and the Founding Editor-in-Chief of Harvard Data Science Review. Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (20042012) and the Dean of Graduate School of Arts and Sciences (20122017). His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multiresolution inferences) to statistical methods and computation (e.g., posterior predictive pvalue; EM algorithm; MCMC; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng was named the best statistician under the age of 40 by Committee of Presidents of Statistical Societies (COPSS) in 2001, and he was elected to the American Academy of Arts and Sciences in 2020.
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