notes

index

Bayesian data analysis by Andrew Gelman, Aki Vehtari, et al in 2014

probabilistic inference, books, Andrew Gelman, Aki Vehtari

course

materials

code
- https://github.com/avehtari/BDA%5Fcourse%5FAalto
- https://github.com/avehtari/BDA%5Fpy%5Fdemos
- https://github.com/avehtari/BDA%5FR%5Fdemos

lectures
- https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx#folderID=%22f0ec3a25-9e23-4935-873b-a9f401646812%22

instructions

contents

Covers parts I-III, which is chapters 1-12 except for 8 in order 1, 2, 3 | 10, 11, 12 | 5, 6, 7, 9 | 4

Parts IV Regression Models and V Nonlinear and nonparametric models contain many examples

Background (Ch 1, Lecture 1)

Single-parameter models (Ch 2, Lecture 2)
- topics
- demos

Multiparameter models (Ch 3, Lecture 3)

Computational methods (Ch 10 , Lecture 4)

Markov chain Monte Carlo (Chs 11-12, Lectures 5-6)

Extra material for Stan and probabilistic programming (see below, Lecture 6)

Hierarchical models (Ch 5, Lecture 7)

Model checking (Ch 6, Lectures 8-9)
- Visualization in Bayesian workflow

Evaluating and comparing models (Ch 7)
- Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
- Videos and case studies
- Cross-validation FAQ

Decision analysis (Ch 9, Lecture 10)

Large sample properties and Laplace approximation (Ch 4, Lecture 11-12)

contents

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