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Bayesian analysis

Bayesian analysis

Highlights

  • Bayesian estimation—thousands of built-in models, by combining
    • 10 likelihood models
    • 18 prior distributions
    • Continuous, binary, ordinal, and count outcomes
    • Univariate, multivariate, and multiple-equation models
    • Linear models, nonlinear models, and generalized nonlinear models
    • Continuous univariate, multivariate, and discrete priors
    • Cannot find the model you need? Write your own.
  • MCMC methods
    • Adaptive Metropolis–Hastings (MH)
    • Adaptive MH with Gibbs updates—hybrid
    • Full Gibbs sampling for some models
  • Graphical tools to check MCMC convergence
  • Posterior summaries: means, medians, SDs, MCSEs, CrIs
  • Hypothesis testing: interval, model posterior probabilities
  • Model comparison: DIC, Bayes factors
  • Save your MCMC and estimation results for future use
See the complete list »


What's this about?

You may be interested in Bayesian analysis if

  • you have some prior information available from previous studies that you would like to incorporate in your analysis. For example, in a study of preterm birthweights, it would be sensible to incorporate the prior information that the probability of a mean birthweight above 15 pounds is negligible. Or,
  • your research problem may require you to answer a question: What is the probability that my parameter of interest belongs to a specific range? For example, what is the probability that an odds ratio is between 0.2 and 0.5? Or,
  • you want to assign a probability to your research hypothesis. For example, what is the probability that a person accused of a crime is guilty?
  • And more.

Overview of Bayesian analysis.

Stata 14 provides a new suite of features for performing Bayesian analysis. Stata's bayesmh fits a variety of Bayesian regression models using an adaptive Metropolis–Hastings (MH) Markov chain Monte Carlo (MCMC) method. Gibbs sampling is also supported for selected likelihood and prior combinations. Commands for checking convergence and efficiency of MCMC, for obtaining posterior summaries for parameters and functions of parameters, for hypothesis testing, and for model comparison are also provided.

Let's see it work

Your Bayesian analysis can be as simple or as complicated as your research problem. Here's an overview.

Normal model with known variance

Suppose we want to estimate the mean car mileage mpg. Our standard frequentist analysis may fit a regression model to mpg and look at the constant _cons.

. regress mpg

Source SS df MS Number of obs = 74
F(0, 73) = 0.00
Model 0 0 . Prob > F = .
Residual 2443.45946 73 33.4720474 R-squared = 0.0000
Adj R-squared = 0.0000
Total 2443.45946 73 33.4720474 Root MSE = 5.7855
mpg Coef. Std. Err. t P>|t| [95% Conf. Interval]
_cons 21.2973 .6725511 31.67 0.000 19.9569 22.63769

Estimation

To fit a Bayesian model, in addition to specifying a distribution or a likelihood model for the outcome of interest, we must also specify prior distributions for all model parameters.

For simplicity, let's model mpg using a normal distribution with a known variance of, say, 35 and use a noninformative flat prior (with a density of 1) for the mean parameter {mpg:_cons}.

. bayesmh mpg, likelihood(normal(35)) prior({mpg:_cons}, flat)

Burn-in ...
Simulation ...

Model summary
Likelihood:
mpg ~ normal({mpg:_cons},35)
Prior:
{mpg:_cons} ~ 1 (flat)
Bayesian normal regression MCMC iterations = 12,500 Random-walk Metropolis-Hastings sampling Burn-in = 2,500 MCMC sample size = 10,000 Number of obs = 74 Acceptance rate = .4027 Log marginal likelihood = -233.90324 Efficiency = .1984
Equal-tailed
mpg Mean Std. Dev. MCSE Median [95% Cred. Interval]
_cons 21.30713 .6933058 .015564 21.32559 19.91967 22.64948

bayesmh discarded the first 2,500 burn-in iterations and used the subsequent 10,000 MCMC iterations to produce the results. The estimated posterior mean, the mean of the posterior distribution, of parameter {mpg:_cons} is close to the OLS estimate obtained earlier, as is expected with the noninformative prior. The estimated posterior standard deviation is close to the standard error of the OLS estimate.

The MCSE of the posterior mean estimate is 0.016. The MCSE is about the accuracy of our simulation results. We would like it to be zero, but that would take an infinite number of MCMC iterations. We used 10,000 iterations and have results accurate to about 1 decimal place. That's good enough, but if we wanted more accuracy, we could increase the MCMC sample size.

According to the credible interval, the probability that the mean of mpg is between 19.92 and 22.65 is about 0.95. Although the confidence interval reported on our earlier regression has similar values, it does not have the same probabilistic interpretation.

Because bayesmh uses MCMC, a simulation-based method, the results will be different every time we run the command. (Inferential conclusions should stay the same provided MCMC converged.) You may want to specify a random-number seed for reproducibility in your analysis.

Checking MCMC convergence

The interpretation of our results is valid only if MCMC converged. Let's explore convergence visually.

. bayesgraph diagnostics {mpg:_cons}, histopts(normal)

graph

The trace plot of {mpg:_cons} demonstrates good mixing. The autocorrelation dies off quickly. The posterior distribution of {mpg:_cons} resembles the normal distribution, as is expected for the specified likelihood and prior distributions. We have no reason to suspect nonconvergence.

We can now proceed with further analysis.

Hypothesis testing

We can test an interval hypothesis that the mean mileage is greater than 21.

. bayestest interval {mpg:_cons}, lower(21)

Interval tests     MCMC sample size =    10,000

       prob1 : {mpg:_cons} > 21

Mean Std. Dev. MCSE
prob1 .6735 0.46896 .0099939

The estimated probability of this interval hypothesis is 0.67. This is in contrast with the classical hypothesis testing that provides a deterministic decision of whether to reject the null hypothesis that the mean is greater than 21 based on some prespecified level of significance. Frequentist hypothesis testing does not assign probabilistic statements to the tested hypotheses.

Informative priors

Suppose that based on previous studies, we have prior information that the mean mileage is normally distributed with mean 30 and variance 5. We can easily incorporate this prior information in our Bayesian model. We will also store our MCMC and estimation results for future comparison.

. bayesmh mpg, likelihood(normal(35)) prior({mpg:_cons}, normal(30,5)) 
              saving(prior1_sim)

Burn-in ...
Simulation ...

Model summary
Likelihood:
mpg ~ normal({mpg:_cons},35)
Prior:
{mpg:_cons} ~ normal(30,5)
Bayesian normal regression MCMC iterations = 12,500 Random-walk Metropolis-Hastings sampling Burn-in = 2,500 MCMC sample size = 10,000 Number of obs = 74 Acceptance rate = .409 Log marginal likelihood = -242.58274 Efficiency = .209
Equal-tailed
mpg Mean Std. Dev. MCSE Median [95% Cred. Interval]
_cons 22.0617 .6683529 .014619 22.05628 20.75121 23.39481
file prior1_sim.dta saved . estimates store prior1

This prior resulted in a slight increase of the posterior mean estimate—the prior shifted the estimate toward the specified prior mean of 30.

Suppose that another competing prior is that the mean mileage is normally distributed with mean 20 and variance 4.

. bayesmh mpg, likelihood(normal(35)) prior({mpg:_cons}, normal(20,4)) 
              saving(prior2_sim)

Burn-in ...
Simulation ...

Model summary
Likelihood:
mpg ~ normal({mpg:_cons},35)
Prior:
{mpg:_cons} ~ normal(20,4)
Bayesian normal regression MCMC iterations = 12,500 Random-walk Metropolis-Hastings sampling Burn-in = 2,500 MCMC sample size = 10,000 Number of obs = 74 Acceptance rate = .4113 Log marginal likelihood = -235.7438 Efficiency = .2043
Equal-tailed
mpg Mean Std. Dev. MCSE Median [95% Cred. Interval]
_cons 21.17991 .6658923 .014733 21.17731 19.87617 22.47459
file prior2_sim.dta saved . estimates store prior2

The results using this prior are more similar to the earlier results with the noninformative prior.

Model comparison

We can compare our two models that used different informative priors. Estimation results of the models were stored under prior1 and prior2. To compare the models, we type

. bayesstats ic prior1 prior2

Bayesian information criteria

DIC log(ML) log(BF)
prior1 472.0359 -242.5827 .
prior2 470.8157 -235.7438 6.838942
Note: Marginal likelihood (ML) is computed using Laplace-Metropolis approximation.

The second model has a lower DIC value and is thus preferable.

Bayes factors—log(BF)—are discussed in [BAYES] bayesstats ic. All we will say here is that the value of 6.84 provides very strong evidence in favor of our second model, prior2.

We can also compute posterior probabilities for each model.

. bayestest model prior1 prior2

Bayesian model tests

log(ML) P(M) P(M|y)
prior1 -242.5827 0.5000 0.0011
prior2 -235.7438 0.5000 0.9989
Note: Marginal likelihood (ML) is computed using Laplace-Metropolis approximation.

The posterior probability of the first model is very low compared with that of the second model. In fact, the posterior probability of the first model is near 0, whereas the posterior probability of the second model is near 1.

Normal model with unknown variance

Continuing our car-mileage example, we now relax the assumption of a known variance of the normal distribution and model it as a parameter {var}. We specify a noninformative Jeffreys prior for the variance parameter.

. bayesmh mpg, likelihood(normal({var})) prior({mpg:_cons}, flat) 
                                         prior({var}, jeffreys)

Burn-in ...
Simulation ...

Model summary
Likelihood:
mpg ~ normal({mpg:_cons},{var})
Priors:
{mpg:_cons} ~ 1 (flat)
{var} ~ jeffreys
Bayesian normal regression MCMC iterations = 12,500 Random-walk Metropolis-Hastings sampling Burn-in = 2,500 MCMC sample size = 10,000 Number of obs = 74 Acceptance rate = .2883 Efficiency: min = .08843 avg = .1191 Log marginal likelihood = -234.63956 max = .1499
Equal-tailed
Mean Std. Dev. MCSE Median [95% Cred. Interval]
mpg
_cons 21.30678 .7018585 .023602 21.30086 19.91435 22.72222
var 34.38441 5.787753 .149506 33.73722 24.71946 47.7112

Note that the MCSE for parameter {mpg:_cons} is larger in this model than it was in the model with a fixed variance. As the number of model parameters increases, the efficiency of the MH algorithm decreases, and the task of constructing an efficient algorithm becomes more and more important. In the above, for example, we could have improved the efficiency of MH by specifying the variance parameter in a separate block, block({var}), to be sampled independently of the mean parameter.

Even without adding the blocking, convergence diagnostics for both mean and variance look good.

. bayesgraph diagnostics _all

graph graph

We can compute summaries for linear and nonlinear expressions of our parameters. Let's compute summaries for a standardized mean, which is a function of both the mean parameter and the variance parameter.

. bayesstats summary (mean_std: {mpg:_cons}/sqrt({var}))

Posterior summary statistics                      MCMC sample size =    10,000

    mean_std : {mpg:_cons}/sqrt({var})

Equal-tailed
Mean Std. Dev. MCSE Median [95% Cred. Interval]
mean_std 3.670299 .3183261 .008546 3.661119 3.060899 4.308195

Simple linear regression

bayesmh makes it easy to include explanatory variables in our Bayesian models. The syntax for regressions looks just as it does in other Stata estimation commands. For example, we can include an indicator of whether the car is foreign or domestic when modeling the mean car mileage.

. bayesmh mpg foreign, likelihood(normal({var}))
                       prior({mpg:_cons foreign}, flat)
                       prior({var}, jeffreys)

Burn-in ...
Simulation ...

Model summary
Likelihood:
mpg ~ normal(xb_mpg,{var})
Priors:
{mpg:foreign _cons} ~ 1 (flat) (1)
{var} ~ jeffreys
(1) Parameters are an element of the linear form xb_mpg. Bayesian normal regression MCMC iterations = 12,500 Random-walk Metropolis-Hastings sampling Burn-in = 2,500 MCMC sample size = 10,000 Number of obs = 74 Acceptance rate = .188 Efficiency: min = .06344 avg = .06893 Log marginal likelihood = -227.16451 max = .07414
Equal-tailed
Mean Std. Dev. MCSE Median [95% Cred. Interval]
mpg
foreign 4.987455 1.43297 .054471 5.014443 2.034135 7.775843
_cons 19.81477 .7796195 .030952 19.79542 18.27116 21.35802
var 29.52163 5.304377 .194809 28.82301 20.82704 41.50129

We specified a flat prior for both the constant and the coefficient of foreign.

Multivariate linear regression

We can fit a multivariate normal regression to model two size characteristics of automobiles, —trunk space, trunk, and turn circle, turn,— as a function of where the car is manufactured, foreign, foreign or domestic. The syntax for the regression part of the model is just like the syntax for Stata's mvreg (multivariate regression) command.

We model the covariance matrix of trunk and turn as the matrix parameter {Sigma,matrix}. We specify noninformative normal priors with large variances for all regression coefficients and use Jeffreys prior for the covariance. The MH algorithm has very low efficiencies for sampling covariance matrices, so we use Gibbs sampling instead. The regression coefficients are sampled by using the MH method.

. bayesmh trunk turn = foreign, likelihood(mvnormal({Sigma, matrix})) 
                                prior({trunk:} {turn:}, normal(0,1000))  
                                prior({Sigma, matrix}, jeffreys(2))      
                                block({Sigma, matrix}, gibbs)

Burn-in ...
Simulation ...

Model summary
Likelihood:
trunk turn ~ mvnormal(2,xb_trunk,xb_turn,{Sigma,m})
Priors:
{trunk:foreign _cons} ~ normal(0,1000) (1)
{turn:foreign _cons} ~ normal(0,1000) (2)
{Sigma,m} ~ jeffreys(2)
(1) Parameters are an element of the linear form xb_trunk. (2) Parameters are an element of the linear form xb_turn. Bayesian multivariate normal regression MCMC iterations = 12,500 Metropolis-Hastings and Gibbs sampling Burn-in = 2,500 MCMC sample size = 10,000 Number of obs = 74 Acceptance rate = .5998 Efficiency: min = .05162 avg = .3457 Log marginal likelihood = -410.2743 max = .7758
Equal-tailed
Mean Std. Dev. MCSE Median [95% Cred. Interval]
trunk
foreign -3.348432 1.056294 .046491 -3.337957 -5.417821 -1.375893
_cons 14.75301 .5450302 .019877 14.73202 13.73661 15.89116
turn
foreign -6.004471 .8822641 .038093 -5.991697 -7.751608 -4.267331
_cons 41.42375 .4817673 .017979 41.42091 40.48585 42.42505
Sigma_1_1 17.11048 3.028132 .036471 16.77325 12.21387 23.93075
Sigma_2_1 7.583515 2.026102 .024179 7.39855 4.153189 12.07798
Sigma_2_2 12.537 2.175963 .024705 12.29787 9.014795 17.45602

Watch how to fit this model using the GUI.

Nonlinear model: Change-point analysis

As an example of a nonlinear model, we consider a change-point analysis of the British coal-mining disaster dataset for the period of 1851 to 1962. This example is adapted from Carlin, Gelfand, and Smith (1992). In these data, the count variable records the number of disasters involving 10 or more deaths.

The graph below suggests a fairly abrupt decrease in the rate of disasters around the 1887–1895 period.

graph

Let's estimate the date when the rate of disasters changed.

We will fit the model

count ~ Poisson(mu1), if year < cp
count ~ Poisson(mu2), if year >= cp

cp—the change point—is the main parameter of interest.

We will use noninformative priors for the parameters: flat priors for the means and a uniform on [1851,1962] for the change point.

We will model the mean of the Poisson distribution as a mixture of mu1 and mu2.

. bayesmh count, likelihood(dpoisson({mu1}*sign(year<{cp})+{mu2}*sign(year>={cp}))) 
                 prior({mu1 mu2}, flat)                          
                 prior({cp}, uniform(1851,1962))                 
                 initial({mu1 mu2} 1 {cp} 1906)

Burn-in ...
Simulation ...

Model summary
Likelihood:
count ~ poisson({mu1}*sign(year<{cp})+{mu2}*sign(year>={cp}))
Hyperpriors:
{mu1 mu2} ~ 1 (flat)
{cp} ~ uniform(1851,1962)
Bayesian Poisson model MCMC iterations = 12,500 Random-walk Metropolis-Hastings sampling Burn-in = 2,500 MCMC sample size = 10,000 Number of obs = 112 Acceptance rate = .2281 Efficiency: min = .03747 avg = .06763 Log marginal likelihood = -173.29271 max = .1193
Equal-tailed
Mean Std. Dev. MCSE Median [95% Cred. Interval]
mu1 3.118753 .3001234 .015504 3.110907 2.545246 3.72073
cp 1890.362 2.4808 .071835 1890.553 1886.065 1896.365
mu2 .9550596 .1209208 .005628 .9560248 .7311639 1.219045

The change point is estimated to have occurred in 1890 with the corresponding 95% CrI of [1886,1896].

We may also be interested in estimating the ratio between the two means.

. bayesstats summary (ratio: {mu1}/{mu2})

Posterior summary statistics                      MCMC sample size =    10,000

       ratio : {mu1}/{mu2}

Equal-tailed
Mean Std. Dev. MCSE Median [95% Cred. Interval]
ratio 3.316399 .5179103 .027848 3.270496 2.404047 4.414975

After 1890, the mean number of disasters decreased by a factor of about 3.3 with a 95% credible range of [2.4, 4.4].

The interpretation of our change-point results is valid only if MCMC converged. We can explore convergence visually.

. bayesgraph diagnostics {cp} (ratio: {mu1}/{mu2})

graph graph

The graphical diagnostics for {cp} and the ratio look reasonable. The marginal posterior distribution of the change point has the main peak at about 1890 and two smaller bumps around the years 1886 and 1896, which correspond to local peaks in the number of disasters.

Using the GUI to perform Bayesian analysis

In Multivariate linear regression, we showed you how to use the command line to fit a Bayesian multivariate regression. Watch Graphical user interface for Bayesian analysis to see how to fit this model and more using the GUI.

Reference

Carlin, B. P., A. E. Gelfand, and A. F. M. Smith. 1992. Hierarchical Bayesian analysis of changepoint problems. Journal of the Royal Statistical Society, Series C 41: 389–405.

Tell me more

Stata's new Bayesian analysis features are documented in their own new 255-page manual. You can read more about Bayesian analysis, more about Stata's new Bayesian features, and see many worked examples in Stata Bayesian Analysis Reference Manual.

Read the overview from the Stata News and In the Spotlight: Bayesian "random effects" models.

Read the Stata Blog entries Bayesian modeling: Beyond Stata's built-in models and Gelman–Rubin convergence diagnostic using multiple chains.

Watch Bayesian analysis in Stata
Watch Introduction to Bayesian analysis, part 1: The basic concepts
Watch Introduction to Bayesian analysis, part 2: MCMC and the Metropolis-Hastings algorithm

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