Handbook of Markov Chain Monte Carlo (Hardcover, 2 ed)

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