Diffusive nested sampling

Sampling diffusive transition paths.

The authors address the problem of sampling double-ended diffusive paths. The ensemble of paths is expressed using a symmetric version of the Onsager-Machlup formula, which only requires evaluation of the force field and which, upon direct time discretization, gives rise to a symmetric integrator that is accurate to second order. Efficiently sampling this ensemble requires avoiding the well-kno...

Title Sampling diffusive transition paths

Nested sampling with demons

This article looks at Skilling’s nested sampling from a physical perspective and interprets it as a microcanonical demon algorithm. Using key quantities of statistical physics we investigate the performance of nested sampling on complex systems such as Ising, Potts and protein models. We show that releasing multiple demons helps to smooth the truncated prior and eases sampling from it because t...

Properties of nested sampling

SUMMARY Nested sampling is a simulation method for approximating marginal likelihoods proposed by Skilling (2006). We establish that nested sampling has an approximation error that vanishes at the standard Monte Carlo rate and that this error is asymptotically Gaussian. We show that the asymptotic variance of the nested sampling approximation typically grows linearly with the dimension of the p...

Slice sampling in nested IBP

We develop a nonparametric Bayesian method that explores the infinite space of latent features and finds the best subset in the sense of posterior probability. When the data appear in several groups, there should be different measures reflecting the differences between the groups. We formalize this as a nested Indian buffet process (nIBP) by assuming different measures according to the specific...