{
  "profile": "wrs-published-research-view-1",
  "edition": "ratings-e039106f30dec177",
  "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS",
  "entry": {
    "id": "hastings-sampling",
    "title": "Hastings: general proposal-based Markov-chain sampling",
    "summary": "Generalized the Metropolis construction through proposal probabilities and acceptance ratios chosen to preserve a target distribution, while treating dependence and estimation error in Markov-chain samples.",
    "boundary": "The normalizing constant can cancel from the ratio, but validity still depends on the chain and target assumptions. Correlated draws are not independent samples, and detailed balance does not supply a finite-time mixing guarantee.",
    "area": "Monte Carlo methods",
    "topic": "Mathematics & logic",
    "year": 1970,
    "lane": "HUMAN",
    "result": "ALGORITHM",
    "package_id": "hastings-sampling",
    "target": {
      "record_id": "urn:uuid:67195dbd-2d9b-472c-a1d2-7028175f5411",
      "version": 2,
      "content_hash": "sha256:3d3eacff289f6ce0d4733f4b1cc41f1132e8641d7a61ff21605b97e0f1d22d30"
    },
    "humans": [
      "W. K. Hastings"
    ],
    "authors_complete": true,
    "human_role_note": "Listed source contributors; individual labor shares are not measured",
    "agents": [],
    "related": [
      "metropolis-sampling"
    ],
    "subresults": [],
    "scientific_assessment": {
      "source_review": "SOURCE_CURATED",
      "scientific_review": "NOT_REVIEWED_BY_WRS",
      "formal_replay": "NOT_RUN",
      "provider_attestation": "NONE"
    },
    "sources": [
      {
        "id": "hastings-1970",
        "title": "Hastings, Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika 57(1), 97–109 (1970)",
        "url": "https://probability.ca/hastings/hastings.pdf"
      }
    ],
    "geography": {
      "country_codes": [
        "CA"
      ],
      "location_basis": "Documented research locations or paper affiliations for this contribution. Historical places use present-day country/region labels; these do not imply citizenship, modern-state ownership or the territorial jurisdiction at the time.",
      "source_url": "https://probability.ca/hastings/hastings.pdf",
      "target": {
        "record_id": "urn:uuid:67195dbd-2d9b-472c-a1d2-7028175f5411",
        "version": 2,
        "content_hash": "sha256:3d3eacff289f6ce0d4733f4b1cc41f1132e8641d7a61ff21605b97e0f1d22d30"
      },
      "basis": "RESEARCH_LOCATION_OR_PAPER_AFFILIATION",
      "reviewed_on": "2026-09-26",
      "nationality_inferred": false,
      "sources": [
        {
          "title": "Attribution source",
          "url": "https://probability.ca/hastings/hastings.pdf"
        }
      ],
      "coverage_status": "COMPLETE",
      "country_labels": {
        "CA": "Canada"
      }
    },
    "points": "85000",
    "published_grade": {
      "id": "hastings-sampling",
      "entry_id": "hastings-sampling",
      "band": "A",
      "modifier": null,
      "target": {
        "record_id": "urn:uuid:67195dbd-2d9b-472c-a1d2-7028175f5411",
        "version": 2,
        "content_hash": "sha256:3d3eacff289f6ce0d4733f4b1cc41f1132e8641d7a61ff21605b97e0f1d22d30"
      },
      "what_changed": "Generalized the Metropolis construction through proposal probabilities and acceptance ratios chosen to preserve a target distribution, while treating dependence and estimation error in Markov-chain samples.",
      "scope": "Generalized the Metropolis construction through proposal probabilities and acceptance ratios chosen to preserve a target distribution, while treating dependence and estimation error in Markov-chain samples. Scope limitation: The normalizing constant can cancel from the ratio, but validity still depends on the chain and target assumptions. Correlated draws are not independent samples, and detailed balance does not supply a finite-time mixing guarantee.",
      "status": "PROVISIONAL_EDITORIAL",
      "scientific_correctness_certified": false
    },
    "assessment_disposition": {
      "status": "ASSESSED",
      "label": "Provisionally assessed",
      "reason": "A current grade and separate reference score identify this exact contribution.",
      "target": {
        "record_id": "urn:uuid:67195dbd-2d9b-472c-a1d2-7028175f5411",
        "version": 2,
        "content_hash": "sha256:3d3eacff289f6ce0d4733f4b1cc41f1132e8641d7a61ff21605b97e0f1d22d30"
      },
      "edition": "ratings-e039106f30dec177"
    }
  },
  "version": {
    "kind": "PUBLIC_RECORD_VIEW",
    "previous": {
      "content_hash": "sha256:384d0ce05cd1bf50b4a4262cc5a2a2073f128c94abaabc0d98b689b78faccc69",
      "record_id": "urn:uuid:67195dbd-2d9b-472c-a1d2-7028175f5411",
      "version": 1
    },
    "published_at": "2026-09-12T00:00:00Z",
    "publisher": {
      "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33",
      "role": "WRS_EDITORIAL_PUBLICATION"
    },
    "record": {
      "contributors": [
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "W. K. Hastings",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Hastings, Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika 57(1), 97–109 (1970)",
            "url": "https://probability.ca/hastings/hastings.pdf"
          }
        }
      ],
      "evidence": [
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-0",
          "kind": "source_reference",
          "source": {
            "citation": "Hastings, Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika 57(1), 97–109 (1970)",
            "url": "https://probability.ca/hastings/hastings.pdf"
          }
        }
      ],
      "origin": "HUMAN_LEGACY",
      "record_profile": "wrs-local-research-notes-0.1",
      "record_type": "research_note",
      "rrp_version": "0.1",
      "scope": {
        "description": "The normalizing constant can cancel from the ratio, but validity still depends on the chain and target assumptions. Correlated draws are not independent samples, and detailed balance does not supply a finite-time mixing guarantee.",
        "domain": "Monte Carlo methods"
      },
      "sources": [
        {
          "citation": "Hastings, Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika 57(1), 97–109 (1970)",
          "url": "https://probability.ca/hastings/hastings.pdf"
        }
      ],
      "statement": "# Hastings: general proposal-based Markov-chain sampling\n\nGeneralized the Metropolis construction through proposal probabilities and acceptance ratios chosen to preserve a target distribution, while treating dependence and estimation error in Markov-chain samples.\n\n## Scientific scope\n\nThe normalizing constant can cancel from the ratio, but validity still depends on the chain and target assumptions. Correlated draws are not independent samples, and detailed balance does not supply a finite-time mixing guarantee.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nW. K. Hastings\n\nListed source contributors; individual labor shares are not measured\n\n\n\n## Original sources\n\n- [Hastings, Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika 57(1), 97–109 (1970)](https://probability.ca/hastings/hastings.pdf)\n",
      "summary": "Generalized the Metropolis construction through proposal probabilities and acceptance ratios chosen to preserve a target distribution, while treating dependence and estimation error in Markov-chain samples.",
      "title": "Hastings: general proposal-based Markov-chain sampling"
    },
    "record_id": "urn:uuid:67195dbd-2d9b-472c-a1d2-7028175f5411",
    "version": 2,
    "source_ref": {
      "record_id": "urn:uuid:67195dbd-2d9b-472c-a1d2-7028175f5411",
      "version": 2,
      "content_hash": "sha256:3d3eacff289f6ce0d4733f4b1cc41f1132e8641d7a61ff21605b97e0f1d22d30"
    },
    "export_view": "wrs-public-editorial-view-1",
    "public_view_hash": "sha256:95cf52259437d4eab4552e02393310ceb8c0be82c44b6f16477eec4062fc808b"
  },
  "target": {
    "record_id": "urn:uuid:67195dbd-2d9b-472c-a1d2-7028175f5411",
    "version": 2,
    "content_hash": "sha256:3d3eacff289f6ce0d4733f4b1cc41f1132e8641d7a61ff21605b97e0f1d22d30"
  },
  "published_grade": {
    "profile": "wrs-published-grade-1",
    "edition": "2026-10-28.1",
    "id": "hastings-sampling",
    "entry_id": "hastings-sampling",
    "revision": 1,
    "title": "Hastings: general proposal-based Markov-chain sampling",
    "context": "Monte Carlo methods",
    "track": "Scoped scientific or research-capability contribution",
    "band": "A",
    "modifier": null,
    "status": "PROVISIONAL_EDITORIAL",
    "assessed_on": "2026-09-14",
    "evidence_cutoff": "2026-09-14",
    "achievement_state": "SUPPORTED_SCOPED_RESULT",
    "what_changed": "Generalized the Metropolis construction through proposal probabilities and acceptance ratios chosen to preserve a target distribution, while treating dependence and estimation error in Markov-chain samples.",
    "scope": "Generalized the Metropolis construction through proposal probabilities and acceptance ratios chosen to preserve a target distribution, while treating dependence and estimation error in Markov-chain samples. Scope limitation: The normalizing constant can cancel from the ratio, but validity still depends on the chain and target assumptions. Correlated draws are not independent samples, and detailed balance does not supply a finite-time mixing guarantee.",
    "evidence_summary": "Assessment uses the catalogue-linked source evidence for the stated contribution. The normalizing constant can cancel from the ratio, but validity still depends on the chain and target assumptions. Correlated draws are not independent samples, and detailed balance does not supply a finite-time mixing guarantee.",
    "references": [
      {
        "title": "Hastings, Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika 57(1), 97–109 (1970)",
        "url": "https://probability.ca/hastings/hastings.pdf",
        "locator": "Retained catalogue source evidence; Summary and sections 1–2: correlated samples, stationary distribution, proposal/acceptance construction and ratio dependence; no full empirical reproduction"
      }
    ],
    "roles": [
      {
        "who": "W. K. Hastings",
        "credit": "SCIENCE",
        "did": "Named source contributor; exact labor shares are not measured."
      }
    ],
    "target": {
      "record_id": "urn:uuid:67195dbd-2d9b-472c-a1d2-7028175f5411",
      "version": 2,
      "content_hash": "sha256:3d3eacff289f6ce0d4733f4b1cc41f1132e8641d7a61ff21605b97e0f1d22d30"
    },
    "scientific_correctness_certified": false,
    "independent_expert_calibration": false,
    "method": "PROPRIETARY",
    "points": "85000"
  },
  "canonical_agent_submission": false,
  "scientific_verification_awarded": false,
  "reader_guide": null,
  "reading_status": "SOURCE_LINKED_NOTE"
}
