{
  "id": "1312.2556",
  "version": "v1",
  "published": "2013-12-06T11:35:14.000Z",
  "updated": "2013-12-06T11:35:14.000Z",
  "title": "A method for importance sampling through Markov chain Monte Carlo with post sampling variational estimate",
  "authors": [
    "A. John Arul",
    "Kannan Iyer"
  ],
  "categories": [
    "stat.CO"
  ],
  "abstract": "We propose a method to efficiently integrate truncated probability densities. The method uses Markov chain Monte Carlo method to sample from a probability density matching the function being integrated. The required normalisation or equivalently the result is obtained by constructing a function with known integral, through non-parametric kernel density estimation and variational procedure. The method is demonstrated with numerical case studies. Possible enhancements to the method and limitations are discussed.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2013-12-06T11:35:14.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "markov chain monte carlo",
      "post sampling variational estimate",
      "chain monte carlo method",
      "integrate truncated probability densities",
      "importance sampling"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2013arXiv1312.2556A"
    }
  }
}