{
  "id": "2104.00753",
  "version": "v1",
  "published": "2021-04-01T20:24:36.000Z",
  "updated": "2021-04-01T20:24:36.000Z",
  "title": "Sampling and statistical physics via symmetry",
  "authors": [
    "Steve Huntsman"
  ],
  "comment": "Proceedings of Les Houches 2020 school on Joint Structures and Common Foundations of Statistical Physics, Information Geometry and Inference for Learning",
  "categories": [
    "cond-mat.stat-mech",
    "math-ph",
    "math.DS",
    "math.MP",
    "math.ST",
    "stat.TH"
  ],
  "abstract": "We formulate both Markov chain Monte Carlo (MCMC) sampling algorithms and basic statistical physics in terms of elementary symmetries. This perspective on sampling yields derivations of well-known MCMC algorithms and a new parallel algorithm that appears to converge more quickly than current state of the art methods. The symmetry perspective also yields a parsimonious framework for statistical physics and a practical approach to constructing meaningful notions of effective temperature and energy directly from time series data. We apply these latter ideas to Anosov systems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-04-01T20:24:36.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "markov chain monte carlo",
      "time series data",
      "well-known mcmc algorithms",
      "art methods",
      "current state"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}