Resources
This page collects some resources to learn about topics in Bayesian computation.
Review articles:
- 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).
- Convergence Diagnostics for Markov Chain Monte Carlo, by Vivekananda Roy (2020).
- An invitation to sequential Monte Carlo samplers, by Chenguang Dai, Jeremy Heng, Pierre E. Jacob, Nick Whiteley (2021).
Books:
- 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).
- Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, by Dani Gamerman, Hedibert F. Lopes (2006)
- Free Energy Computations: a mathematical perspective, by Tony Lelièvre, Mathias Rousset and Gabriel Stoltz (2010).
- Handbook of Approximate Bayesian Computation, edited by Scott A. Sisson, Yanan Fan, Mark Beaumont (2019).
- An Introduction to Sequential Monte Carlo, by Nicolas Chopin, Omiros Papaspiliopoulos (2020).
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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.
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Remote seminars and videos related to Bayesian computation: