Handbook of Markov Chain Monte Carlo

摘要: Foreword Stephen P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng Introduction to MCMC, Charles J. Geyer A short history of Markov chain Monte Carlo: Subjective recollections from in-complete data, Christian Robert George Casella Reversible jump Carlo, Yanan Fan Scott A. Sisson Optimal proposal distributions adaptive Jeffrey S. Rosenthal MCMC using Hamiltonian dynamics, Radford M. Neal Inference Monitoring Convergence, Gelman Kenneth Shirley Implementing MCMC: Estimating with confidence, James Flegal Jones Perfection within reach: Exact sampling, Radu V. Craiu Spatial point processes, Mark Huber The data augmentation algorithm: Theory methodology, Hobert Importance simulated tempering umbrella J.Geyer Likelihood-free in the analysis genetic on related individuals, Elizabeth Thompson Carlo based a multilevel model for functional MRI Brian Caffo, DuBois Bowman, Lynn Eberly, Susan Spear Bassett Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings high-energy astrophysics, David van Dyk Taeyoung Park Posterior exploration computationally intensive forward models, Dave Higdon, C. Shane Reese, Moulton, Jasper Vrugt Colin Fox Statistical ecology, Ruth King Gaussian random field models spatial Murali Haran Modeling preference changes via hidden item response theory model, Jong Hee Parallel Bayesian imputation multiple distributed lag models: case study environmental epidemiology, Roger Peng, Francesca Dominici, Thomas Louis, Zeger state space Paul Fearnhead educational research, Roy Levy, Mislevy, John T. Behrens Applications fisheries science, Russell B. Millar Model comparison simulation hierarchical analyzing rural-urban migration Thailand, Filiz Garip Bruce Western