Bayesian Statistics for Data Scientists
Priors, posteriors, hierarchical shrinkage and Bayesian A/B decisions — with the computation that makes it practical
Do Bayesian inference the way data teams actually use it: Bayes' rule with sequential updating, conjugate Beta-Binomial, Gamma-Poisson and Normal-Normal updates worked by hand (posterior mean as a precision-weighted average, posterior predictive), credible versus confidence intervals (HDI versus equal-tailed, coverage that is not 95 percent everywhere), Bayesian A/B testing with P(B > A), expected loss, ROPE and prior sensitivity, what optional stopping really does to a posterior-threshold rule, hierarchical models with partial pooling and empirical Bayes, the exact link between L2 and a Gaussian prior, computation from grids and Laplace to Metropolis, HMC/NUTS and variational inference with R-hat, ESS and divergence diagnostics, prior and posterior predictive checks with LOO, Bayesian regression, and industry patterns such as Beta lower-bound ranking. Includes a two-variant test and a five-country shrinkage example whose numbers were run in NumPy, SciPy and PyMC.
Practice questions (6)
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Derive the Beta-Binomial Update and Use It for an Email Campaign
Intermediate · Free -
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Compute the Probability B Beats A and the Expected Loss for a Checkout Test
Intermediate · Free -
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Design and Defend Priors for a New-Checkout Conversion Test
Advanced -
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Explain Shrinkage to a Product Manager Who Distrusts Adjusted Numbers
Intermediate -
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Your Hierarchical Model Reports R-hat 1.3 and 18 Effective Samples: What Now?
Advanced