Fit Bayesian regression 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 tools are available to check convergence,
including multiple chains. 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. Compute model fit using posterior predictive p-values.
Generate predictions. And much more.
Learn about Bayesian analysis and see examples of Bayesian features.
See New in Bayesian analysis.
Also see an Overview example.
- 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
- Simply type bayes: in front of any of 46
estimation commands to fit Bayesian regression models
- Change any of the default priors
- Change any of the simulation or sampling
- Time-series operators
- Control Panel lets you specify and fit models from an
- Multiple chains New
- Use GUI to fit models
- Use command language to fit models
Classes of models
- Generalized (location-scale) t
- Inverse gamma
- Pareto New
- Multivariate normal
- Dirichlet New
- Inverse Wishart
- Geometric New
- User-defined density
- User-defined log density
- Specialized priors
Add your own models
- Write your own programs to calculate likelihood function and choose built-in priors
- Write your own programs to calculate posterior density directly
- Use built-in adaptive MH sampling to simulate marginal posterior
Markov chain Monte Carlo (MCMC) methods
- Adaptive Metropolis-Hastings (MH)
- Hybrid MH (adaptive MH with Gibbs updates)
- Full Gibbs sampling for some models
Adaptive MH sampling
- Blocking of parameters
- Adaptation within each block
- Diminishing adaptation
- Random-effects parameters
- Control scale and covariance of the proposal distribution
- Control adaptation
- Length of adaptation
- Maximum and minimum numbers of adaptive iterations
- Acceptance rate
- Adaptation rate
- Target acceptance rate
- Acceptance rate tolerance
- May specify for some or all parameters
- May specify for some or all chains New
- View and run all postestimation features for your command
- Automatically updated as estimation commands are run
New in Stata 16
for more about what was added in Stata 16.