Introduction to Bayesian Statistics Using Stata
Loading...
Can’t use the file because of accessibility barriers? Contact us with the title of the item, permanent link, and specifics of your accommodation need.
Date
2019-09-20
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Indiana University Workshop in Methods
Permanent Link
Abstract
Bayesian analysis has become a popular tool for many statistical applications. Yet many data analysts have little training in the theory of Bayesian analysis and software used to fit Bayesian models. This talk will provide an intuitive introduction to the concepts of Bayesian analysis and demonstrate how to fit Bayesian models using Stata. No prior knowledge of Bayesian analysis is necessary and specific topics will include the relationship between likelihood functions, prior, and posterior distributions, Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm, and how to use Stata’s Bayes prefix to fit Bayesian models.
Description
Chuck Huber is Associate Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health. In addition to working with Stata’s team of software developers, he produces instructional videos for the Stata Youtube channel, writes blog entries, develops online NetCourses and gives talks about Stata at conferences and universities. Most of his current work is focused on statistical methods used by behavioral and health scientists. He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition and birth defects. Dr. Huber currently teaches introductory biostatistics at Texas A&M where he previously taught categorical data analysis, survey data analysis, and statistical genetics.
Keywords
research methods, statistical methods, Workshop in Methods, Bayesian statistics
Citation
Journal
DOI
Link(s) to data and video for this item
Rights
Type
Presentation