{
  "id": "2009.06182",
  "version": "v1",
  "published": "2020-09-14T04:07:51.000Z",
  "updated": "2020-09-14T04:07:51.000Z",
  "title": "Density Estimation via Bayesian Inference Engines",
  "authors": [
    "M. P. Wand",
    "J. F. C. Yu"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate that the proposed density estimates have excellent comparative performance and scale well to very large sample sizes due a binning strategy. Moreover, the approach is fully Bayesian and all estimates are accompanied by pointwise credible intervals. An accompanying package in the R language facilitates easy use of the new density estimates.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-09-14T04:07:51.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "density estimation",
      "automatic probability density function estimates",
      "contemporary bayesian inference engines",
      "effective automatic probability density function",
      "density estimates"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}