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
Learn about 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 New
- Simply type bayes: in front of any of 45
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
- Use GUI to fit models
- Use command language to fit models
Classes of models
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
New in Stata 15
for more about what was added in Stata 15.