Bayesian modelling
Outline
Content
Exercises
Evaluations
Notes
Course content
12: Review
Overview
Readings, lectures, and videos
Installation
R
and RStudio
R
packages
Course content
1: Introduction
2: Bayesics
3: Priors
4: Monte Carlo, Markov chains and Metropolis-Hastings
5: Gibbs sampling
6: Bayesian workflow
7: Probabilistic programming
8: Bayesian regression
9: Deterministic approximations
10: Variational inference
11: Expectation propagation
12: Review
On this page
Slides
Course content
12: Review
Final Review
Practice final
and
solutions
.
Practice midterm
Slides
View all slides in new window
Download PDF of all slides