»  Home »  Products »  Features »  Bayesian analysis

## Bayesian analysis

Fit Bayesian models using one of the Markov chain Monte Carlo (MCMC) methods.You can choose from a variety of supported models or even program your own. Extensive graphical tools are available to check convergence visually. Compute posterior mean estimates and credible intervals for model parameters and functions of model parameters. You can perform both interval and model-based hypothesis testing. Compare models using Bayes factors. And much more.

Also see an Overview example.

Estimation Updated

• Thousands of built-in models, by combining
• over 50 likelihood models, including univariate and multivariate normal, logit, probit, ordered logit, ordered probit, Poisson ...
• Many prior distributions, including normal, lognormal, multivariate normal, gamma, beta, Wishart ...
• Continuous, binary, ordinal, and count outcomes
• Univariate, multivariate, and multiple-equation models
• Linear and nonlinear models
• Continuous univariate, multivariate, and discrete priors
• bayes: prefix New
• Simply type bayes: in front of any of 45 estimation commands to fit Bayesian regression models
• Change any of the default priors
• Change any of the simulation or sampling settings
• Time-series operators
• Control Panel lets you specify and fit models from an easy-to-use interface
• Use GUI to fit models
• Use command language to fit models

Classes of models Updated

Likelihood models Updated

Prior distributions

Markov chain Monte Carlo (MCMC) methods

• Full Gibbs sampling for some models

• Blocking of parameters
• Random-effects parameters
• Control scale and covariance of the proposal distribution
• Maximum and minimum numbers of adaptive iterations
• Acceptance rate
• Target acceptance rate
• Acceptance rate tolerance

Simulation

Starting values

• Automatic
• May specify for some or all parameters
• Write your own programs to calculate likelihood function and choose built-in priors
• Write you own programs to calculate posterior density directly
• Use built-in adaptive MH sampling to simulate marginal posterior
• View and run all postestimation features for your command
• Automatically updated as estimation commands are run

Graphical tools to check MCMC convergence

Tools to check MCMC efficiency

• Effective sample sizes
• Autocorrelation times
• Efficiencies
• Compute any of above for parameters or functions of parameters

Posterior summaries

MCSE estimation methods

• using effective sample size
• using batch means

Hypothesis testing

Factor variables Updated

• Automatically create indicators based on categorical variables
• Form interactions among discrete and continuous variables
• Include polynomial terms
Watch Introduction to Factor Variables in Stata tutorials