Notes for BDA3
Bayesian Data Analysis course
I Notes
Introduction
1 Section 1. Course introduction and prerequisites
- 1.1 Resources
- 1.2 Notes
  - 1.2.1 Reading instructions
  - 1.2.2 Lecture notes
2 Section 2. Basics of Bayesian inferences
- 2.1 Resources
- 2.2 Notes
3 Section 3. Multidimensional Posterior
- 3.1 Resources
- 3.2 Notes
4 Section 4. Monte Carlo
- 4.1 Resources
- 4.2 Notes
5 Section 5. Markov chain Monte Carlo
- 5.1 Resources
- 5.2 Notes
6 Section 6. HMC, NUTS, and Stan
- 6.1 Resources
- 6.2 Notes
7 Section 7. Hierarchical models and exchangeability
- 7.1 Resources
- 7.2 Notes
8 Section 8. Model checking & Cross-validation
- 8.1 Resources
- 8.2 Notes
9 Section 9. Model comparison and selection
- 9.1 Resources
- 9.2 Notes
  - 9.2.1 Lecture notes
10 Section 10. Decision analysis
- 10.1 Resources
- 10.2 Notes
11 Section 11. Normal approximation & Frequency properties
- 11.1 Resources
- 11.2 Notes
12 Section 12. Extended topics
- 12.1 Resources
- 12.2 Notes
  - 12.2.1 Lecture 12.1 Frequency evaluation, hypothesis testing and variable selection
  - 12.2.2 Lecture 12.2 Overview of modeling data collection, BDA3 Ch 8, linear models, BDA Ch 14-18, lasso, horseshoe and Gaussian processes, BDA3 Ch 21
13 Section 13. Notes on ‘Ch 14. Introduction to regression models’
14 Section 14. Notes on ‘Ch 15. Hierarchical linear models’
- 14.1 Chapter 15. Hierarchical linear models
15 Section 17. Notes on ‘Ch 19. Parametric nonlinear models’
- 15.1 Chapter 19. Parametric nonlinear models
  - 19.1 Example: serial dilution assay
  - 19.2 Example: population toxicokinetics
16 Section 18. Notes on ‘Ch 20. Basis function models’
17 Section 19. Notes on ‘Ch 21. Gaussian process models’
- 17.1 Chapter 21. Gaussian process models
18 Section 20. Notes on ‘Ch 22. Finite mixture models’
- 18.1 Chapter 22. Finite mixture models
  - 22.1 Setting up and interpreting mixture models
  - 22.4 Unspecifed number of mixture components
19 Section 21. Notes on ‘Ch 23. Dirichlet process models’
- 19.1 Chapter 23. Dirichlet process models
II Assignments
Introduction
20 Assignment 1
21 Assignment 2
- 21.1 Setup
- 21.2 Exercise 1. Inference for binomial proportion
22 Assignment 3
23 Assignment 4
- 23.1 Setup
- 23.2 Exercise 1. Bioassay model
24 Assignment 5
- 24.1 Setup
- 24.2 Generalized linear model: Bioassay with Metropolis
25 Assignment 6
- 25.1 Setup
- 25.2 Exercise 1. Generalized linear model: Bioassay with Stan
26 Assignment 7
27 Assignment 8
- 27.1 Setup
- 27.2 Exercise 1. Model assessment: LOO-CV for factory data with Stan
28 Assignment 9
- 28.1 Setup
- 28.2 Exercise 1. Decision analysis for the factory data
III Book Exercises
Introduction
29 Chapter 1 Exercises
30 Chapter 2 Exercises
31 Chapter 5 Exercises
- 31.1 Question 1
32 Chapter 19 Exercises - Reproducing the the ‘serial dilution assay’
IV Models
33 Stan models
V Misc
About
- 33.8 The website
- 33.9 About me
References
Published with bookdown

Introduction

The sections in this part of the book are for the nine course assignments. Some may be missing a question or two because it is a text-based answer that I was confident in my answer, too.