The importance of step size for Random Walk Metropolis

Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence

An introduction to the Random Walk Metropolis algorithm

1. Probability Models and Axioms

Constrained parameters? Use Metropolis-Hastings

Why we typically use dependent sampling to sample from the posterior

6. Monte Carlo Simulation

How to derive a Gibbs sampling routine in general

Understanding Metropolis-Hastings algorithm

What are divergent iterations and what to do about them?

The illusion of uninformative priors

An introduction to Gibbs sampling

The problems with using simple Monte Carlo to determine the marginal likelihood

16. Learning: Support Vector Machines

Adaptive MCMC For Everyone

How to code up a model with discrete parameters in Stan

An introduction to rejection sampling

An introduction to importance sampling - optimal importance distributions

Estimating the posterior predictive distribution by sampling

5. Stochastic Processes I

11. Introduction to Machine Learning