Lecture 2. Probability and Bayes’ theorem

Statistical Data Analysis

Dmitry V. Naumov (JINR)

Draft Outline

• TODO: axioms of probability and conditional probability
• TODO: Bayes’ theorem and posterior updates
• TODO: simple physics examples

Exercises

Exercise

Show that the likelihood for a Poisson count \(n\) with expectation \(\mu\) is \[ L(\mu)=\frac{\mu^n e^{-\mu}}{n!}. \]