pith. machine review for the scientific record. sign in

arxiv: cond-mat/0008226 · v2 · submitted 2000-08-16 · ❄️ cond-mat.stat-mech · cond-mat.dis-nn· hep-lat

Recognition: unknown

Population Monte Carlo algorithms

Authors on Pith no claims yet
classification ❄️ cond-mat.stat-mech cond-mat.dis-nnhep-lat
keywords algorithmscarlomontefilterpopulationannealedcalledcarried
0
0 comments X
read the original abstract

We give a cross-disciplinary survey on ``population'' Monte Carlo algorithms. In these algorithms, a set of ``walkers'' or ``particles'' is used as a representation of a high-dimensional vector. The computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by lots of different terms -- ``quantum Monte Carlo'', ``transfer-matrix Monte Carlo'', ``Monte Carlo filter (particle filter)'',``sequential Monte Carlo'' and ``PERM'' etc. Here we discuss them in a coherent framework. We also touch on related algorithms -- genetic algorithms and annealed importance sampling.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Tensor-Network Population Annealing

    cond-mat.stat-mech 2026-04 conditional novelty 7.0

    TNPA uses tensor-network contractions only in a reliable temperature window to seed population annealing, with an effective-sample-size diagnostic to pick the switch-over temperature.