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Full Description
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.
Contents
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
