Module 2 — Bayesian Updating

Contents

Module 2 — Bayesian Updating#

Coming soon. This module will show — with animation — how adding data narrows a prior into a posterior.

Preview#

Bayes’ theorem in one sentence: the posterior is proportional to the likelihood times the prior.

Key ideas this module will cover:

  • Walking through Bayes’ theorem with a coin-flip analogy

  • Applying the same logic to TEX86 calibration data

  • Why more data = narrower posterior (but not always)

  • What happens when data conflict with the prior

Contents