Hamiltonian Monte Carlo

Exploring the limitations of Gibbs sampling, Rob highlights the challenges of achieving accurate expected values with infinite sample sizes. He introduces Hamiltonian Monte Carlo as a more efficient alternative, capable of converging with fewer samples. This method, originally developed for molecular dynamics, offers a promising solution to the common pathologies faced in hierarchical models.