Matthew Hoffman and Andrew Gelman have posted a paper on arXiv entitled “The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo” and developing an improvement on the Hamiltonian Monte Carlo algorithm called NUTS (!). Here is the abstract: Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) ... [Read more...]

Peng Yu sent me an email about the conditions for convergence of a Gibbs sampler: The following statement mentions convergence. But I’m not familiar what the regularity condition is. “But it is necessary to have a finite probability of moving away from the current state at all times in ... [Read more...]

An interesting note was arXived a few days ago by Madeleine Thompson and Radford Neal. Beside the nice touch of mixing crumbs and slices, the neat idea is to have multiple-try proposals for simulating within a slice and to decrease the dimension of the simulation space at each try. This ... [Read more...]

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