It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.
– Sherlock Holmes, “A Scandal in Bohemia”
Bayesian Inference: A Statistician's Explainer
Bayesian inference is more than just a statistical method; it is a mathematically rigorous framework for updating our beliefs in the face of new evidence. This explainer walks through the core components of Bayes’ theorem—Prior, Likelihood, and Posterior—and demonstrates how it contrasts with traditional frequentist statistics. We cover intuitive examples, the mathematical machinery, and why Bayesian methods are increasingly powering modern data science and machine learning.