This course will cover some basics of Bayesian methods and computing algorithms, with the emphasis on Markov chain Monte Carlo. It will begin with the introduction to Bayesian statistics, and cover normal and non-normal approximation to likelihood and posteriors, the EM algorithm, data augmentation, and finally, Markov Chain Monte Carlo (MCMC) methods. We will cover more advanced MCMC algorithms in the last three weeks. Biostatistics and environmental examples will be given throughout the course. There will be weekly homeworks, and students will be expected to complete a full-scale real-data project by the end of the course. There will be a final in-class presentation. In terms of programming language, C, C++, Matlab or R can be used to implement the algorithms and carry out data analyses.
Prerequisites: