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Introduction to Bayesian Analysis Using Stata


Learn to use Stata to perform basic Bayesian analysis. Bayesian analysis is a statistical paradigm that answers research questions about unknown parameters using probability statements. For example, what is the probability that a person accused of a crime is guilty? What is the probability that treatment A is more cost effective than treatment B for a specific healthcare provider? What is the probability that the odds ratio is between 0.3 and 0.5? And many more. Such probabilistic statements are natural to Bayesian analysis because of the underlying assumption that all parameters are random quantities. In Bayesian analysis, a parameter is summarized by an entire distribution of values instead of one fixed value as in classical frequentist analysis. Estimating this distribution, a posterior distribution of a parameter of interest, is at the heart of Bayesian analysis.

This course will provide an introduction to Bayesian analysis, demonstrate its use in several applications, and introduce Stata's suite of commands for conducting Bayesian analysis.

Price: $1,295  

We offer a 15% discount for group enrollments of three or more participants.

Course leader

Gustavo Sanchez portrait

Gustavo Sánchez is a Senior Econometrician and Director of the Technical Services department at StataCorp LLC. He has a master's degree in econometrics from Southampton University, UK, and he got his PhD in agricultural economics at Texas A&M University. Gustavo worked at the Central Bank of Venezuela, and he was a professor of econometrics at the Universidad Central de Venezuela.

Gustavo has been an instructor for a few time-series and panel-data courses using Stata. He taught a workshop on Bayesian analysis using Stata at the 2019 American Political Science Association (APSA), and he taught a course on Bayesian analysis using Stata at the 2020 ICSPR summer program. He has also given webinars on Bayesian analysis using Stata at a few Stata conferences. In October 2021, he gave a talk on Bayes VAR analysis using Stata at the Stata Mexican conference.

Course topics

  • Introduction to Bayesian analysis
    • Motivating example
    • What is Bayesian analysis?
    • Why Bayesian analysis?
    • Advantages and disadvantages of Bayesian analysis
  • Bayesian statistics
    • Prior and posterior distributions
    • Point estimation
    • Interval estimation
    • Monte Carlo standard error (MCSE)
    • Model comparison
    • Prior selection
  • Markov chain Monte Carlo (MCMC)
    • What is MCMC?
    • Why MCMC?
    • Adaptive Metropolis–Hastings and Gibbs MCMC sampling
    • Convergence of MCMC
    • Efficiency of MCMC
    • Multiple chains
  • Bayesian analysis in Stata
    • Stata's Bayesian suite of commands
    • Fitting basic Bayesian models using the bayesmh command
    • Convergence diagnostics
    • Posterior summaries
    • Credible intervals
    • Deviance information criterion (DIC)
    • Bayes factors
    • Sensitivity analysis to the choice of prior
  • Bayesian regression
    • Fitting regression models using the bayes prefix
    • Linear regression
    • Autoregressive models
    • Logistic regression
    • Other regression models


Basic knowledge of statistics and regression analysis and a working knowledge of Stata.

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Enrollment is limited. This course is offered in both classroom and web-based settings.

Classroom training courses are two-day courses that run from 8:30 a.m. to 4:30 p.m. each day. These courses take place at a training center where computers with Stata installed are provided. A continental breakfast, lunch, and an afternoon snack will also be provided; the breakfast is available before the course begins.

Web-based training courses are four-day courses that run for three and a half hours each day. You will be provided with a temporary Stata license to install on your computer, a printed copy of the course notes, and all the course datasets so that you can easily follow along. Learn more about how our web-based training courses work, watch a video demonstration, and find technical requirements for participating in this type of training.





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