This repository has course material for Bayesian Data Analysis course at Aalto (CS-E5710)
The material will be updated during the course. Exercises instructions and slides will be updated at latest on Monday of the corresponding week.
- Basic terms of probability theory
- probability, probability density, distribution
- sum, product rule, and Bayes' rule
- expectation, mean, variance, median
- in Finnish, see e.g. Stokastiikka ja tilastollinen ajattelu
- in English, see e.g. Wikipedia and Introduction to probability and statistics
- Some algebra and calculus
- Basic visualisation techniques (R or Python)
- histogram, density plot, scatter plot
- see e.g. BDA_R_demos
- see e.g. BDA_py_demos
- Background (Ch 1)
- Single-parameter models (Ch 2)
- Multiparameter models (Ch 3)
- Computational methods (Ch 10)
- Markov chain Monte Carlo (Ch 11--12)
- Extra material for Stan and probabilistic programming
- Hierarchical models (Ch 5)
- Model checking (Ch 6)
- Evaluating and comparing models (Ch 7)
- Decision analysis (Ch 9)
- Large sample properties and Laplace approximation (Ch 4)
- In addition you learn workflow for Bayesian data analysis
Exercises (67%) and a project work (33%). Minimum of 50% of points must be obtained from both the exam and the exercises.
We recommend using R in the course as there are more packages for Stan in R. If you are already fluent in Python, but not in R, then using Python is probably easier. Unless you are already experienced and have figured out your preferred way to work with R, we recommend installing RStudio Desktop.