Prior to Posterior - Interactive Visualization

Bayesian inference updates beliefs as data arrives: posterior ∝ likelihood × prior. The Beta-Binomial model is the canonical example: Beta prior + Binomial likelihood = Beta posterior. Start with a prior belief about coin fairness (α, β), then observe coin flips. Each flip updates the posterior - more data swamps the prior. This is the essence of Bayesian learning.

• Set prior beliefs with α and β sliders for the Beta distribution
• Click Flip Coin to observe one flip and update posterior live
• Watch posterior narrow as more flips are observed (uncertainty reduces)
• See that strong priors (large α, β) require more data to be overcome
• Foundation for Bayesian neural networks, Thompson sampling, and A/B testing

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.