This page collects some resources to learn about topics in Bayesian computation.
- Bayesian computation: a perspective on the current state, and sampling backwards and forwards, by Peter J. Green, Krzysztof Łatuszyński, Marcelo Pereyra, Christian P. Robert (2015).
- The Hastings algorithm at fifty, by David Dunson, James Johndrow (2020).
- General state space Markov chains and MCMC algorithms, by Gareth Roberts, Jeff Rosenthal (2004).
- Markov Chain Monte Carlo for Statistical Inference, by Julian Besag (2001).
- A History of Markov Chain Monte Carlo--Subjective Recollections from Incomplete Data, by Christian Robert, George Casella (2011).
- Stochastic simulation, by Brian Ripley (1987).
- Handbook of Markov Chain Monte Carlo , edited by Steve Brooks, Andrew Gelman, Galin L. Jones and Xiao-Li Meng (2011).
- Monte Carlo Statistical Methods, by Christian Robert, George Casella (2004).
- An Introduction to Sequential Monte Carlo, by Nicolas Chopin, Omiros Papaspiliopoulos (2020).
Websites, blogs and lists of references:
- David Wilson's page on CFTP.
- Wilfrid Kendall's page on perfect simulation.
- Arnaud Doucet's list of references on Sequential Monte Carlo.
- Pierre Del Moral's page on Feynman-Kac models and interacting particle systems.
- Joris Bierkens' page on Piecewise Deterministic Monte Carlo.
Remote seminars and videos related to Bayesian computation: