Primers
A curated collection of posts providing technical/thematic context for most of the material on this site.
Preliminaries
- Probability and Uncertainty
- Conditional Probabilities
- Relationships between Probability Distributions
- Maximum Likelihood Estimation
- Monte Carlo Integration
- Properties of Markov Chains
Statistical Modeling
- Bayesian Modeling
- Developing a Coherent Bayesian Workflow
- Generative Models
- How to choose a Prior
- Strategies for Model Selection
Algorithms for Inference
- The Basics of Sampling
- Markov Chain Monte Carlo
- Expectation Maximization
- Stochastic Gradient Descent
- Simulated Annealing
- Parallel Tempering
- Variational Autoencoders