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Stata's new xtmlogit command models categorical outcomes that change over time.
Now you can do both!
Frequentist analysis is entirely data-driven and strongly depends on whether or not the data
assumptions required by the model are met. On the other hand, Bayesian analysis provides a more
robust estimation approach by using not only the data at hand but also some existing information or
knowledge about model parameters.
In frequentist statistics, estimators are used to approximate the true values of the unknown parameters,
whereas Bayesian statistics provides an entire distribution of the parameters. In our example of a
prevalence of an infectious disease from What is Bayesian analysis?, frequentist analysis produced one
point estimate for the prevalence, whereas Bayesian analysis estimated the entire posterior distribution
of the prevalence based on a given sample.
Description
This entry provides a software-free introduction to Bayesian analysis. See [BAYES] bayes for an
overview of the software for performing Bayesian analysis and for an overview example.
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