Bayesian Inference
نویسنده
چکیده
The Bayesian interpretation of probability is one of two broad categories of interpretations. Bayesian inference updates knowledge about unknowns, parameters, with information from data. The LaplacesDemon package in R enables Bayesian inference, and this vignette provides an introduction to the topic. This article introduces Bayes’ theorem, model-based Bayesian inference, components of Bayesian inference, prior distributions, hierarchical Bayes, conjugacy, likelihood, numerical approximation, prediction, Bayes factors, model fit, posterior predictive checks, and ends by comparing advantages and disadvantages of Bayesian inference.
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