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# Introduction to Bayesian Analysis Using Stata

## April 13 : 08:00 - April 16 : 17:00 CEST

$1295Learn 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. 15% discount for group enrollments of three or more participants.

## 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 MetropolisāHastings 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

## Prerequisite

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

**Event Conditions**

Course registration is binding. Upon cancellation more than 8 working days before the course start date, we invoice 50% of the course fee. Upon cancellation less than 8 working days before the course start date, we invoice the full course fee. Click here to read the full Terms and Conditions!