I was reading Christian Robert’s Blog today and quite liked the new Metropolis-Hastings algorithm he was discussing. It seemed simple and easy to implement.
Whenever I code up MCMC, I tend to stick with very basic MH algorithms, such as independent moves or random walks on the log scale.
What MH algorithms do people routinely use? In particular:
- Why do you use them?
- In some sense you must think that they are optimal – after all you use them routinely! So how do you judge optimality: ease-of-coding, convergence, …
I’m particularly interested in what is used in practice, i.e. when you code up your own schemes.
Hybrid Monte Carlo is the standard algorithm used for neural networks. Gibbs sampling for Gaussian process classification (when not using a deterministic approximation instead).