{
  "id": "0911.0221",
  "version": "v1",
  "published": "2009-11-02T01:10:38.000Z",
  "updated": "2009-11-02T01:10:38.000Z",
  "title": "Limit theorems for some adaptive MCMC algorithms with subgeometric kernels: Part II",
  "authors": [
    "Yves F. Atchade",
    "Gersende Fort"
  ],
  "comment": "34 pages",
  "categories": [
    "math.PR",
    "math.ST",
    "stat.CO",
    "stat.TH"
  ],
  "abstract": "We prove a central limit theorem for a general class of adaptive Markov Chain Monte Carlo algorithms driven by sub-geometrically ergodic Markov kernels. We discuss in detail the special case of stochastic approximation. We use the result to analyze the asymptotic behavior of an adaptive version of the Metropolis Adjusted Langevin algorithm with a heavy tailed target density.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2009-11-02T01:10:38.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "60J10",
      "65C05"
    ],
    "keywords": [
      "adaptive mcmc algorithms",
      "limit theorem",
      "subgeometric kernels",
      "chain monte carlo algorithms driven",
      "markov chain monte carlo algorithms"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 34,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2009arXiv0911.0221A"
    }
  }
}