Stata 15 help for bayesian_estimation

[BAYES] bayesian estimation -- Bayesian estimation commands


Bayesian estimation in Stata is similar to standard estimation -- simply prefix the estimation commands with bayes: (see [BAYES] bayes). You can also refer to [BAYES] bayesmh and [BAYES] bayesmh evaluators for fitting more general Bayesian models.

The following estimation commands support the bayes prefix.

Command Entry Description ------------------------------------------------------------------------- Linear regression models regress [BAYES] bayes: regress Linear regression hetregress [BAYES] bayes: hetregress Heteroskedastic linear regressions tobit [BAYES] bayes: tobit Tobit regression intreg [BAYES] bayes: intreg Interval regression truncreg [BAYES] bayes: truncreg Truncated regression mvreg [BAYES] bayes: mvreg Multivariate regression

Binary-response regression models logistic [BAYES] bayes: logistic Logistic regression, reporting odds ratios logit [BAYES] bayes: logit Logistic regression, reporting coefficients probit [BAYES] bayes: probit Probit regression cloglog [BAYES] bayes: cloglog Complementary log-log regression hetprobit [BAYES] bayes: hetprobit Heteroskedastic probit regressions binreg [BAYES] bayes: binreg GLM for the binomial family biprobit [BAYES] bayes: biprobit Bivariate probit regression

Ordinal-response regression models ologit [BAYES] bayes: ologit Ordered logistic regression oprobit [BAYES] bayes: oprobit Ordered probit regression zioprobit [BAYES] bayes: zioprobit Zero-inflated ordered probit regression

Categorical-response regression models mlogit [BAYES] bayes: mlogit Multinomial (polytomous) logistic regression mprobit [BAYES] bayes: mprobit Multinomial probit regression clogit [BAYES] bayes: clogit Conditional logistic regression

Count-response regression models poisson [BAYES] bayes: poisson Poisson regression nbreg [BAYES] bayes: nbreg Negative binomial regression gnbreg [BAYES] bayes: gnbreg Generalized negative binomial regression tpoisson [BAYES] bayes: tpoisson Truncated Poisson regression tnbreg [BAYES] bayes: tnbreg Truncated negative binomial regression zip [BAYES] bayes: zip Zero-inflated Poisson regression zinb [BAYES] bayes: zinb Zero-inflated negative binomial regression

Generalized linear models glm [BAYES] bayes: glm Generalized linear models

Fractional-response regression models fracreg [BAYES] bayes: fracreg Fractional response regression betareg [BAYES] bayes: betareg Beta regression

Survival regression models streg [BAYES] bayes: streg Parametric survival models

Sample-selection regression models heckman [BAYES] bayes: heckman Heckman selection model heckprobit [BAYES] bayes: heckprobit Probit model with sample selection heckoprobit [BAYES] bayes: heckoprobit Ordered probit model with sample selection

Multilevel regression models mixed [BAYES] bayes: mixed Multilevel linear regression metobit [BAYES] bayes: metobit Multilevel tobit regression meintreg [BAYES] bayes: meintreg Multilevel interval regression melogit [BAYES] bayes: melogit Multilevel logistic regression meprobit [BAYES] bayes: meprobit Multilevel probit regression mecloglog [BAYES] bayes: mecloglog Multilevel complementary log-log regression meologit [BAYES] bayes: meologit Multilevel ordered logistic regression meoprobit [BAYES] bayes: meoprobit Multilevel ordered probit regression mepoisson [BAYES] bayes: mepoisson Multilevel Poisson regression menbreg [BAYES] bayes: menbreg Multilevel negative binomial regression meglm [BAYES] bayes: meglm Multilevel generalized linear model mestreg [BAYES] bayes: mestreg Multilevel parametric survival regression -------------------------------------------------------------------------

Video examples

Introduction to Bayesian statistics, part 1: The basic concepts

Introduction to Bayesian statistics, part 2: MCMC and the Metropolis-Hastings algorithm

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