Handbook of Markov Chain Monte Carlo 1st Edition by Steve Brooks,Andrew Gelman,Galin Jones,Xiao Li Meng ISBN 1420079417 978-1420079418

Handbook of Markov Chain Monte Carlo 1st Edition by Steve Brooks, Andrew Gelman, Galin Jones, Xiao-Li Meng – Ebook PDF Instant Download/Delivery: 1420079417, 978-1420079418

Full download Handbook of Markov Chain Monte Carlo 1st Edition after payment

 

Product details:

ISBN 10: 1420079417 

ISBN 13: 978-1420079418

Author: Steve Brooks, Andrew Gelman, Galin Jones, Xiao-Li Meng

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 fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory.

The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology.

The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments.

Table of contents: 

1. Foreword
2. Introduction to MCMC
3. A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data
4. Reversible jump Markov chain Monte Carlo
5. Optimal proposal distributions and adaptive MCMC
6. MCMC using Hamiltonian dynamics
7. Inference and Monitoring Convergence
8. Implementing MCMC: Estimating with confidence
9. Perfection within reach: Exact MCMC sampling
10. Spatial point processes
11. The data augmentation algorithm: Theory and methodology
12. Importance sampling, simulated tempering, and umbrella sampling
13. Likelihood-free Markov chain Monte Carlo
14. MCMC in the analysis of genetic data on related individuals
15. A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data
16. Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings in high-energy astrophysics
17. Posterior exploration for computationally intensive forward models
18. Statistical ecology
19. Gaussian random field models for spatial data
20. Modeling preference changes via a hidden Markov item response theory model
21. Parallel Bayesian MCMC imputation for multiple distributed lag models: A case study in environmental epidemiology
22. MCMC for state space models
23. MCMC in educational research
24. Applications of MCMC in fisheries science
25. Model comparison and simulation for hierarchical models: analyzing rural-urban migration in Thailand

People also search for:

    
handbook of markov chain monte
    
handbook of markov chain monte carlo pdf
    
handbook of markov chain monte carlo second edition
    
handbook of markov chain monte carlo 2nd edition
    
handbook of markov chain monte carlo neal

Tags:

Steve Brooks,Andrew Gelman,Galin Jones,Xiao Li Meng,Handbook of Markov,Chain Monte Carlo