Article thumbnail

Adaptive Metropolis sampling and optimization with product distributions

By David H. Wolpert and Chiu Fan Lee


The Metropolis-Hastings (MH) algorithm is a way to IID sample a provided target distribution π(x). It works by repeatedly sampling a separate proposal distribution T (x, x ′) to generate a random walk {x(t)} which converges to a set of samples of π. Here, we introduce a T-updating phase after the cooling period and before sampling begins. In the updating phase, {x(t)} is used to update T at t and our update method corresponds to the information-theoretically optimal meanfield approximation to π. We employ our algorithm to sample the energy distribution for several spin-glasses and we demonstrate the superiority of our algorithm to the conventional MH algorithm

Year: 2005
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • http://collectives.stanford.ed... (external link)

  • To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

    Suggested articles