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