Module 1: Foundations

Sample Space & Events

Defining outcomes, events, and axiomatic definitions.

10 min Completed

Python: Random Variables

Simulating discrete and continuous variables using NumPy.

15 min Completed

02

Module 2: Bayesian Inference

Priors & Posteriors

Mathematical derivation of Bayes theorem and conjugate priors.

20 min In Progress

Markov Chain Monte Carlo

Introduction to MCMC sampling methods.

25 min Unread

03

Module 3: Hypothesis Testing

Null vs Alternative

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