{
  "id": "2102.10583",
  "version": "v1",
  "published": "2021-02-21T11:04:10.000Z",
  "updated": "2021-02-21T11:04:10.000Z",
  "title": "Inverse Gaussian Process regression for likelihood-free inference",
  "authors": [
    "Hongqiao Wang",
    "Ziqiao Ao",
    "Tengchao Yu",
    "Jinglai Li"
  ],
  "categories": [
    "stat.CO",
    "stat.ME"
  ],
  "abstract": "In this work we consider Bayesian inference problems with intractable likelihood functions. We present a method to compute an approximate of the posterior with a limited number of model simulations. The method features an inverse Gaussian Process regression (IGPR), i.e., one from the output of a simulation model to the input of it. Within the method, we provide an adaptive algorithm with a tempering procedure to construct the approximations of the marginal posterior distributions. With examples we demonstrate that IGPR has a competitive performance compared to some commonly used algorithms, especially in terms of statistical stability and computational efficiency, while the price to pay is that it can only compute a weighted Gaussian approximation of the marginal posteriors.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-02-21T11:04:10.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "inverse gaussian process regression",
      "likelihood-free inference",
      "bayesian inference problems",
      "marginal posterior distributions",
      "computational efficiency"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}