Bayesian Inference & Statistical Modeling
Probability & Statistics
Master Bayesian statistical inference, prior/posterior distributions, MCMC methods, and hierarchical modeling.
Learning Objectives
- Apply Bayes' theorem to update beliefs with data
- Choose and specify prior distributions
- Implement MCMC sampling methods
- Build hierarchical Bayesian models
Lessons
1
Bayes' Theorem & Probability Fundamentals
35 min
2
Prior Distributions: Conjugate & Non-informative
35 min
3
Likelihood Functions & Posterior Derivation
35 min
4
Markov Chain Monte Carlo: Metropolis-Hastings
35 min
5
Gibbs Sampling & Hamiltonian Monte Carlo
35 min
6
Hierarchical Models & Partial Pooling
35 min
7
Bayesian Model Comparison & Decision Theory
35 min
Quick Practice
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Key Concept Flashcards
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