Literature

You are not expected to buy or read textbooks for this part of the module, but if you do wish to read around the subject, some suggestions are as follows.

• A Gelman, J B Carlin, H S Stern, D B Dunson, A Vehtari and D B Rubin (2013) Bayesian Data Analysis (3rd Edition), Chapman and Hall/CRC.
• B Lambert (2018) A Student’s Guide to Bayesian Analysis, SAGE Publications.
• M Betancourt (2018) A Conceptual Introduction to Hamiltonian Monte Carlo, ArXiv.
• S Brooks, A Gelman, G L Jones and X Meng (2011) Handbook of Markov Chain Monte Carlo, Chapman and Hall/CRC.
• J J Faraway (2016) Extending the Linear Model with R: Generalised Linear, Mixed Effects and Nonparametric Regression Models (2nd Edition), Chapman and Hall/CRC.
• R J A Little and D B Rubin (2020) Statistical Analysis with Missing Data (3rd Edition), Wiley.
• Zhou and Reiter (2010) A Note on Bayesian Inference After Multiple Imputation, The American Statistician.