Peter Hoff (2009). A first course in Bayesian statistical methods. Springer.
Brian J. Reich & Sujit K. Ghosh (2019). Bayesian Statistical Methods. CRC Press.
Christian P. Robert (2007). The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation. Springer (2nd Edition).
This is an introductory course on Bayesian statistics for graduate students. The course introduces the Bayesian paradigm and focus on Bayesian modeling, computation, and inference. We first convey the ideology of Bayesian statistics which is a particular approach to statistical inference that differs philosophically and operationally from the classic frequentist approach. We then define Bayesian inference and discuss its advantages. Detailed applications are illustrated using some classical models, including binomial, Poisson, univariate normal, multivariate normal model, and linear regression. We go through each step of building Bayesian hierarchical models and apply Bayes’ theorem to derive posterior distributions. To inference on posterior distributions, MCMC algorithm is introduced as a modern method of approximating posteriors.
Graphical and descriptive methods in statistics, probability, random variables and distributions,
sampling, estimation, hypothesis testing, regression, analysis of variance, exploratory and diagnostics,
statistical computing.
Graphical and descriptive methods in statistics, probability, random variables and distributions,
sampling, estimation, hypothesis testing, regression, analysis of variance, exploratory and diagnostics,
statistical computing.
Graphical and descriptive methods in statistics, probability, random variables and distributions,
sampling, estimation, hypothesis testing, regression, analysis of variance, exploratory and diagnostics,
statistical computing.
Graphical and descriptive methods in statistics, probability, random variables and distributions,
sampling, estimation, hypothesis testing, regression, analysis of variance, exploratory and diagnostics, statistical computing.