{
  "id": "1311.5699",
  "version": "v3",
  "published": "2013-11-22T10:25:16.000Z",
  "updated": "2015-03-25T11:08:29.000Z",
  "title": "Computational inference beyond Kingman's coalescent",
  "authors": [
    "Jere Koskela",
    "Paul A. Jenkins",
    "Dario Spano"
  ],
  "comment": "20 pages, 5 figures, to appear in Journal of Applied Probability 52.2 (June 2015)",
  "categories": [
    "math.PR",
    "q-bio.PE",
    "stat.CO"
  ],
  "abstract": "Full likelihood inference under Kingman's coalescent is a computationally challenging problem to which importance sampling (IS) and the product of approximate conditionals (PAC) method have been applied successfully. Both methods can be expressed in terms of families of intractable conditional sampling distributions (CSDs), and rely on principled approximations for accurate inference. Recently, more general $\\Lambda$- and $\\Xi$-coalescents have been observed to provide better modelling fits to some genetic data sets. We derive families of approximate CSDs for finite sites $\\Lambda$- and $\\Xi$-coalescents, and use them to obtain \"approximately optimal\" IS and PAC algorithms for $\\Lambda$-coalescents, yielding substantial gains in efficiency over existing methods.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2014-06-09T15:26:52.000Z",
      "abstract": "Full likelihood inference under Kingman's coalescent is a computationally challenging problem to which importance sampling (IS) and the product of approximate conditionals (PAC) method have been applied successfully. Both methods can be expressed in terms of families of intractable conditional sampling distributions (CSDs), and rely on principled approximations for accurate inference. Recently, more general $\\Lambda$-- and $\\Xi$--coalescents have been observed to provide better modelling fits to some genetic data sets. We derive families of approximate CSDs for finite sites $\\Lambda$-- and $\\Xi$--coalescents, and use them to obtain \"approximately optimal\" IS and PAC algorithms for $\\Lambda$--coalescents, yielding substantial gains in efficiency over existing methods.",
      "journal": null,
      "doi": null
    },
    {
      "version": "v3",
      "updated": "2015-03-25T11:08:29.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "60G09",
      "92D25",
      "93E10"
    ],
    "keywords": [
      "kingmans coalescent",
      "computational inference",
      "full likelihood inference",
      "genetic data sets",
      "approximate conditionals"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 20,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2013arXiv1311.5699K"
    }
  }
}