Constrained parameters? Use Metropolis-Hastings

9:21
The importance of step size for Random Walk Metropolis

32:09
The intuition behind the Hamiltonian Monte Carlo algorithm

36:03
[Gibbs sampler and MCMC] MCMC diagnostics

18:58
An introduction to Gibbs sampling

8:54
Germany’s Far-Right Comeback | NYT Opinion

11:28
An introduction to the Random Walk Metropolis algorithm

21:31
Logistic Regression: An Easy and Clear Beginner’s Guide

1:19:49