{
  "id": "1102.2171",
  "version": "v2",
  "published": "2011-02-10T16:58:37.000Z",
  "updated": "2011-06-03T20:55:28.000Z",
  "title": "CLTs and asymptotic variance of time-sampled Markov chains",
  "authors": [
    "Krzysztof Latuszynski",
    "Gareth O. Roberts"
  ],
  "comment": "A small simulation illustrating theoretical results added",
  "categories": [
    "math.PR",
    "stat.CO"
  ],
  "abstract": "For a Markov transition kernel $P$ and a probability distribution $ \\mu$ on nonnegative integers, a time-sampled Markov chain evolves according to the transition kernel $P_{\\mu} = \\sum_k \\mu(k)P^k.$ In this note we obtain CLT conditions for time-sampled Markov chains and derive a spectral formula for the asymptotic variance. Using these results we compare efficiency of Barker's and Metropolis algorithms in terms of asymptotic variance.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2011-06-03T20:55:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "asymptotic variance",
      "markov transition kernel",
      "time-sampled markov chain evolves",
      "probability distribution",
      "clt conditions"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2011arXiv1102.2171L"
    }
  }
}