{
  "id": "2004.04620",
  "version": "v1",
  "published": "2020-04-08T02:16:40.000Z",
  "updated": "2020-04-08T02:16:40.000Z",
  "title": "Bayesian Computation with Intractable Likelihoods",
  "authors": [
    "Matthew T. Moores",
    "Anthony N. Pettitt",
    "Kerrie Mengersen"
  ],
  "comment": "arXiv admin note: text overlap with arXiv:1503.08066",
  "categories": [
    "stat.CO"
  ],
  "abstract": "This article surveys computational methods for posterior inference with intractable likelihoods, that is where the likelihood function is unavailable in closed form, or where evaluation of the likelihood is infeasible. We review recent developments in pseudo-marginal methods, approximate Bayesian computation (ABC), the exchange algorithm, thermodynamic integration, and composite likelihood, paying particular attention to advancements in scalability for large datasets. We also mention R and MATLAB source code for implementations of these algorithms, where they are available.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-04-08T02:16:40.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "62F15",
      "62M40"
    ],
    "keywords": [
      "intractable likelihoods",
      "article surveys computational methods",
      "matlab source code",
      "approximate bayesian computation",
      "pseudo-marginal methods"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}