{
  "annotation_id": "82c0c021-9a0b-4c9b-a6b8-d4532f5e5c14",
  "date_created": "2026-03-02T18:01:24.523000Z",
  "date_modified": "2026-03-02T18:01:24.523000Z",
  "file_hash": "f722af7d5611a1b0ec38bde1c3e8281645ba934f4b460c3a238a3695a40c1d01",
  "private": false,
  "record": {
    "abstract": "In this paper we demonstrate that multi-modal Probability Distribution\nFunctions (PDFs) may be efficiently sampled using an algorithm originally\ndeveloped for numerical integrations by Monte-Carlo methods. This algorithm can\nbe used to generate an input PDF which can be used as an independence sampler\nin a Metropolis-Hastings chain to sample otherwise troublesome\ndistributions.Some examples in one two and five dimensions are worked out.",
    "arxiv_id": "physics/9903044",
    "authors": [
      "K. J. Abraham",
      "L. M. Haines"
    ],
    "categories": [
      "physics.comp-ph"
    ],
    "doi": "10.1006/jcph.1999.6343",
    "title": "A New Technique for Sampling Multi-Modal Distributions",
    "url": "https://arxiv.org/abs/physics/9903044"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "638e0386-0311-402a-939a-4c937c47474e",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}