What are divergent iterations and what to do about them?
14:35
How to code up a bespoke probability density in Stan
20:28
Centered versus non-centered hierarchical models
32:09
The intuition behind the Hamiltonian Monte Carlo algorithm
9:24
Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence
21:35
How to code up a model with discrete parameters in Stan
11:28
An introduction to the Random Walk Metropolis algorithm
17:13
Effective sample size: representing the cost of dependent sampling
44:48