{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "stan-probabilistic-language-2017", "title": "Stan imperative probabilistic model language and inference runtime", "summary": "Compiles imperative model specifications into log-density computations and supplies adaptive HMC/NUTS sampling, optimization and diagnostics through language interfaces.", "boundary": "The2017journal account describes version2.14.0 and earlier project work, not a2017initial software launch or the current runtime. Continuous-parameter model restrictions, transformations and finite-sampling diagnostics remain; HMC/NUTS and optimization methods are inherited. Distinct from the lower-level Stan Math autodifferentiation library.", "area": "Probabilistic programming", "topic": "Artificial intelligence", "year": 2017, "lane": "HUMAN", "result": "ALGORITHM", "package_id": "stan-probabilistic-language-2017", "target": { "record_id": "urn:uuid:db01cdcb-bab8-4ab9-a4fb-5d7256cded11", "version": 1, "content_hash": "sha256:05f488b50086326c78d551b254bee6819928776917ad99998d39612796ea521d" }, "humans": [ "Bob Carpenter", "Andrew Gelman", "Matthew D. Hoffman", "Daniel Lee", "Ben Goodrich", "Michael Betancourt", "Marcus A. Brubaker", "Jiqiang Guo", "Peter Li", "Allen Riddell" ], "authors_complete": true, "human_role_note": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. 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Hoffman", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Daniel Lee", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Ben Goodrich", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Michael Betancourt", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Marcus A. Brubaker", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Jiqiang Guo", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Peter Li", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Allen Riddell", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "The2017journal account describes version2.14.0 and earlier project work, not a2017initial software launch or the current runtime. Continuous-parameter model restrictions, transformations and finite-sampling diagnostics remain; HMC/NUTS and optimization methods are inherited. Distinct from the lower-level Stan Math autodifferentiation library.", "domain": "Probabilistic programming" }, "sources": [ { "citation": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf" } ], "statement": "# Stan imperative probabilistic model language and inference runtime\n\n## The question\n\nSuppose you have observations and a mathematical model containing unknown parameters. You want to express what those parameters could be, given the data and your assumptions. Writing a new sampler for every model is laborious and error-prone. Stan separates specifying a probabilistic model from much of the machinery used to explore its implied parameter distribution.\n\n## The idea\n\nA Stan program declares data and parameters and accumulates a log density, a convenient numerical representation of relative probability. The system compiles the specification into executable calculations and supplies inference methods and diagnostics. Automatic differentiation computes derivatives needed by gradient-based algorithms. Constraints and transformations help connect natural model parameters with the coordinates used internally by those algorithms.\n\n## Why it matters\n\nThe contribution is a reusable scientific workflow, not a promise that any program produces a sensible analysis. Sampling methods such as Hamiltonian Monte Carlo have their own origins and assumptions. Good software can reveal numerical trouble, yet it cannot decide whether a chosen model represents the question you actually meant to study. A precise answer to an unsuitable model can still mislead.\n\n## A teaching example\n\nFor an original classroom exercise, imagine fifty flips of an idealized coin, with thirty heads. A model might contain one unknown heads probability and an explicit prior assumption. The observed fraction is 30/50 = 0.6, but the inference task also concerns uncertainty and the model assumptions. The exercise is not evidence that a real coin remains unchanged over time or that every sequence of outcomes is independent.\n\n## Scope and a useful next step\n\nStart with a small model whose behavior you can check analytically or by a simple grid calculation. Compare that reference with the numerical inference and inspect its diagnostics. Document the prior, parameter constraints and what would count as model failure. A useful contribution can identify a mismatch between two equivalent-looking specifications before anyone relies on a more complicated application. The2017journal account describes version2.14.0 and earlier project work, not a2017initial software launch or the current runtime. Continuous-parameter model restrictions, transformations and finite-sampling diagnostics remain; HMC/NUTS and optimization methods are inherited. Distinct from the lower-level Stan Math autodifferentiation library.\n\n## Scientific scope\n\nThe2017journal account describes version2.14.0 and earlier project work, not a2017initial software launch or the current runtime. Continuous-parameter model restrictions, transformations and finite-sampling diagnostics remain; HMC/NUTS and optimization methods are inherited. Distinct from the lower-level Stan Math autodifferentiation library.\n\n## Sources for this explanation\n\n- [Stan: A Probabilistic Programming Language](https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf): Pinned original contribution and its stated method, assumptions and limits. The teaching example is an original classroom illustration, not a reported experiment.\n", "summary": "Compiles imperative model specifications into log-density computations and supplies adaptive HMC/NUTS sampling, optimization and diagnostics through language interfaces.", "title": "Stan imperative probabilistic model language and inference runtime" }, "record_id": "urn:uuid:db01cdcb-bab8-4ab9-a4fb-5d7256cded11", "version": 1, "source_ref": { "record_id": "urn:uuid:db01cdcb-bab8-4ab9-a4fb-5d7256cded11", "version": 1, "content_hash": "sha256:05f488b50086326c78d551b254bee6819928776917ad99998d39612796ea521d" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:f43b3b232a58c7d8d7e13f1e86fa8bbf7f7e5d16d486efc869d1d07dd7f595d8" }, "target": { "record_id": "urn:uuid:db01cdcb-bab8-4ab9-a4fb-5d7256cded11", "version": 1, "content_hash": "sha256:05f488b50086326c78d551b254bee6819928776917ad99998d39612796ea521d" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "stan-probabilistic-language-2017", "entry_id": "stan-probabilistic-language-2017", "revision": 1, "title": "Stan imperative probabilistic model language and inference runtime", "context": "Probabilistic programming", "track": "Human-designed research method or resource", "band": "S", "modifier": "-", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Compiles imperative model specifications into log-density computations and supplies adaptive HMC/NUTS sampling, optimization and diagnostics through language interfaces.", "scope": "Compiles imperative model specifications into log-density computations and supplies adaptive HMC/NUTS sampling, optimization and diagnostics through language interfaces. The2017journal account describes version2.14.0 and earlier project work, not a2017initial software launch or the current runtime. Continuous-parameter model restrictions, transformations and finite-sampling diagnostics remain; HMC/NUTS and optimization methods are inherited. Distinct from the lower-level Stan Math autodifferentiation library.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The2017journal account describes version2.14.0 and earlier project work, not a2017initial software launch or the current runtime. Continuous-parameter model restrictions, transformations and finite-sampling diagnostics remain; HMC/NUTS and optimization methods are inherited. Distinct from the lower-level Stan Math autodifferentiation library.", "references": [ { "title": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities." } ], "target": { "record_id": "urn:uuid:db01cdcb-bab8-4ab9-a4fb-5d7256cded11", "version": 1, "content_hash": "sha256:05f488b50086326c78d551b254bee6819928776917ad99998d39612796ea521d" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "290000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": { "id": "stan-probabilistic-language-2017", "target": { "record_id": "urn:uuid:db01cdcb-bab8-4ab9-a4fb-5d7256cded11", "version": 1, "content_hash": "sha256:05f488b50086326c78d551b254bee6819928776917ad99998d39612796ea521d" }, "sections": [ { "title": "The question", "text": "Suppose you have observations and a mathematical model containing unknown parameters. You want to express what those parameters could be, given the data and your assumptions. Writing a new sampler for every model is laborious and error-prone. Stan separates specifying a probabilistic model from much of the machinery used to explore its implied parameter distribution." }, { "title": "The idea", "text": "A Stan program declares data and parameters and accumulates a log density, a convenient numerical representation of relative probability. The system compiles the specification into executable calculations and supplies inference methods and diagnostics. Automatic differentiation computes derivatives needed by gradient-based algorithms. Constraints and transformations help connect natural model parameters with the coordinates used internally by those algorithms." }, { "title": "Why it matters", "text": "The contribution is a reusable scientific workflow, not a promise that any program produces a sensible analysis. Sampling methods such as Hamiltonian Monte Carlo have their own origins and assumptions. Good software can reveal numerical trouble, yet it cannot decide whether a chosen model represents the question you actually meant to study. A precise answer to an unsuitable model can still mislead." }, { "title": "A teaching example", "text": "For an original classroom exercise, imagine fifty flips of an idealized coin, with thirty heads. A model might contain one unknown heads probability and an explicit prior assumption. The observed fraction is 30/50 = 0.6, but the inference task also concerns uncertainty and the model assumptions. The exercise is not evidence that a real coin remains unchanged over time or that every sequence of outcomes is independent." }, { "title": "Scope and a useful next step", "text": "Start with a small model whose behavior you can check analytically or by a simple grid calculation. Compare that reference with the numerical inference and inspect its diagnostics. Document the prior, parameter constraints and what would count as model failure. A useful contribution can identify a mismatch between two equivalent-looking specifications before anyone relies on a more complicated application. The2017journal account describes version2.14.0 and earlier project work, not a2017initial software launch or the current runtime. Continuous-parameter model restrictions, transformations and finite-sampling diagnostics remain; HMC/NUTS and optimization methods are inherited. Distinct from the lower-level Stan Math autodifferentiation library." } ], "references": [ { "title": "Stan: A Probabilistic Programming Language", "url": "https://sites.stat.columbia.edu/gelman/research/published/v76i01.pdf", "locator": "Pinned original contribution and its stated method, assumptions and limits. The teaching example is an original classroom illustration, not a reported experiment." } ] }, "reading_status": "EXPLANATORY_GUIDE" }