Biostatistics 590B - Intro/BayesianStatisticalModel

Bayesian modelling approaches provide natural ways for researchers in many disciplines to structure their data and knowledge, and they yield direct and intuitive answers to scientific questions. This course introduces the principles and practice of Bayesian modeling. Students will learn the fundamental concepts of Bayesian inference and the roles of prior, likelihood, and posterior distributions. Students will gain an understanding of sampling-based computational approaches, including Markov Chain Monte Carlo (MCMC). Emphasis is placed on interpreting the results of Bayesian analyses. Throughout the course, students will also develop hands-on skills using statistical software (R and Stan) to conduct basic Bayesian analyses.

STAT111,240/PSY,PUBHL223BIOS54 This course is a course where instruction is delivered across different modalities (https://www.umass.edu/flex/students). Students have the option to attend either in-person (at the Amherst campus) or online. If you choose to attend class virtually, you are required to join the Zoom meeting synchronously.