## Stata 15 help for bayesian_estimation

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[BAYES] bayesian estimation -- Bayesian estimation commands

Description

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
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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
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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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