Learn to use empirical Bayesian methods for estimating binomial proportions, through a series of examples drawn from baseball statistics. These methods are effective in estimating click-through rates on ads, success rates of experiments, and other examples common in modern data science. You'll learn both the theory and the practice behind empirical Bayesian methods, including computing credible intervals, performing Bayesian A/B testing, and fitting mixture models. Each example comes with R code that can be used to analyze your own data.
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