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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 |