{
  "annotation_id": "c1541d82-9df9-4025-9345-c9e8b9e6583f",
  "date_created": "2026-03-02T18:01:18.389000Z",
  "date_modified": "2026-03-02T18:01:18.389000Z",
  "file_hash": "fa91a39bc24ecf57b50267e101e7d21ee98d067500bc37508ddf6048fc9b9b74",
  "private": false,
  "record": {
    "abstract": "We describe a simple Importance Sampling strategy for Monte Carlo simulations\nbased on a least squares optimization procedure. With several numerical\nexamples, we show that such Least Squares Importance Sampling (LSIS) provides\nefficiency gains comparable to the state of the art techniques, when the latter\nare known to perform well. However, in contrast to traditional approaches, LSIS\nis not limited to the determination of the optimal mean of a Gaussian sampling\ndistribution. As a result, it outperforms other methods when the ability to\nadjust higher moments of the sampling distribution, or to deal with\nnon-Gaussian or multi-modal densities, is critical to achieve variance\nreductions.",
    "arxiv_id": "physics/0703181",
    "authors": [
      "Luca Capriotti"
    ],
    "categories": [
      "physics.soc-ph",
      "cond-mat.other",
      "math.ST",
      "physics.comp-ph",
      "q-fin.CP",
      "stat.TH"
    ],
    "title": "Least Squares Importance Sampling for Monte Carlo Security Pricing",
    "url": "https://arxiv.org/abs/physics/0703181"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a835a4f4-8d47-4633-9b7e-1e4ea94366d1",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}