Winter 2018


Michele Guindani
Associate Professor
Department of Statistics
Phone: (949) 824-5968
Office Hours: W 1pm-2:30pm @ Bren Hall 2241

Time and Days

TuTh 9:30-10:50 MSTB 110

Fri 2:00- 2:50p DBH 1300 (according to schedule)


The course will provide a basic introduction to Bayesian concepts and methods with an emphasis on the data analysis. We will discuss model choice, including the assessment of prior distributions. We will discuss how to conduct inference in a Bayesian setting, through posterior means, credible intervals and hypothesis testing.
The Analyses will be performed using the freely available software Jags as implemented in the R packages rjags and R2jags. I will also showcase the use of the package Rstan. I will not cover but I would suggest you to look also at the Nimble suite, which represents a flexible extension of Bugs, Winbugs, and Jags. Both R and RStudio will be required for this class.


Learn basic concepts of Bayesian analysis,, including how to conduct posterior and predictive inference; learn how to use common Bayesian models in applications; learn common ways of prior elicitation; utilize R for Bayesian computation, visualization, and analysis of data.


Prerequisite: STATS 120C. Recommended: STATS 201 or STATS 210.


Christensen R, Johnson W, Branscum A, Hanson T. E. (2011) Bayesian Ideas and Data Analysis, An Introduction for Scientists and Statisticians, CRC press


For PhD students mainly:
Hoff P.D. (2009) A First Course in Bayesian Statistical Methods, Springer