Handbook of Markov Chain Monte Carlo
eBook - PDF

Handbook of Markov Chain Monte Carlo

  1. 619 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Handbook of Markov Chain Monte Carlo

About this book

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

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Yes, you can access Handbook of Markov Chain Monte Carlo by Steve Brooks, Andrew Gelman, Galin Jones, Xiao-Li Meng, Steve Brooks,Andrew Gelman,Galin Jones,Xiao-Li Meng in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Chapman and Hall/CRC
Year
2011
Print ISBN
9781498797863
eBook ISBN
9781420079425
Edition
1
Topic
Mathematics
Subtopic
Probability & Statistics
Index
Mathematics

Table of contents

  1. Front cover
  2. Contents
  3. Preface
  4. Editors
  5. Contributors
  6. Part I. Foundations, Methodology, and Algorithms
  7. Chapter 1. Introduction to Markov Chain Monte Carlo
  8. Chapter 2. A Short History of MCMC: Subjective Recollections from Incomplete Data
  9. Chapter 3. Reversible Jump MCMC
  10. Chapter 4. Optimal Proposal Distributions and Adaptive MCMC
  11. Chapter 5. MCMC Using Hamiltonian Dynamics
  12. Chapter 6. Inference from Simulations and Monitoring Convergence
  13. Chapter 7. Implementing MCMC: Estimating with Confidence
  14. Chapter 8. Perfection within Reach: Exact MCMC Sampling
  15. Chapter 9. Spatial Point Processes
  16. Chapter 10. The Data Augmentation Algorithm: Theory and Methodology
  17. Chapter 11. Importance Sampling, Simulated Tempering, and Umbrella Sampling
  18. Chapter 12. Likelihood-Free MCMC
  19. Part II. Applications and Case Studies
  20. Chapter 13. MCMC in the Analysis of Genetic Dataon Related Individuals
  21. Chapter 14. An MCMC-Based Analysis of a Multilevel Model for Functional MRI Data
  22. Chapter 15. Partially Collapsed Gibbs Sampling and Path-Adaptive Metropolisā€“Hastings in High-Energy Astrophysics
  23. Chapter 16. Posterior Exploration for Computationally Intensive Forward Models
  24. Chapter 17. Statistical Ecology
  25. Chapter 18. Gaussian Random Field Models for Spatial Data
  26. Chapter 19. Modeling Preference Changes via a Hidden MarkovItem Response Theory Model
  27. Chapter 20. Parallel Bayesian MCMC Imputation for Multiple Distributed Lag Models: A Case Study in Environmental Epidemiology
  28. Chapter 21. MCMC for Stateā€“Space Models
  29. Chapter 22. MCMC in Educational Research
  30. Chapter 23. Applications of MCMC in Fisheries Science
  31. Chapter 24. Model Comparison and Simulation for Hierarchical Models: Analyzing Ruralā€“Urban Migrationin Thailand
  32. Index
  33. Back cover