{
  "id": "1010.1595",
  "version": "v3",
  "published": "2010-10-08T05:43:27.000Z",
  "updated": "2011-03-24T10:01:07.000Z",
  "title": "Using parallel computation to improve Independent Metropolis--Hastings based estimation",
  "authors": [
    "Pierre Jacob",
    "Christian P. Robert",
    "Murray H. Smith"
  ],
  "comment": "19 pages, 8 figures, to appear in Journal of Computational and Graphical Statistics",
  "categories": [
    "stat.CO",
    "cs.DC",
    "cs.DS"
  ],
  "abstract": "In this paper, we consider the implications of the fact that parallel raw-power can be exploited by a generic Metropolis--Hastings algorithm if the proposed values are independent. In particular, we present improvements to the independent Metropolis--Hastings algorithm that significantly decrease the variance of any estimator derived from the MCMC output, for a null computing cost since those improvements are based on a fixed number of target density evaluations. Furthermore, the techniques developed in this paper do not jeopardize the Markovian convergence properties of the algorithm, since they are based on the Rao--Blackwell principles of Gelfand and Smith (1990), already exploited in Casella and Robert (1996), Atchade and Perron (2005) and Douc and Robert (2010). We illustrate those improvements both on a toy normal example and on a classical probit regression model, but stress the fact that they are applicable in any case where the independent Metropolis-Hastings is applicable.",
  "revisions": [
    {
      "version": "v3",
      "updated": "2011-03-24T10:01:07.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "parallel computation",
      "estimation",
      "generic metropolis-hastings algorithm",
      "classical probit regression model",
      "improvements"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 19,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2010arXiv1010.1595J"
    }
  }
}