{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/proposal-of-a-score-based-approach-to","title":"Proposal of a Score Based Approach to Sampling Using Monte Carlo Estimation of Score and Oracle Access to Target Density","arxiv_id":"2212.03325","date":"2022-12-06","proceeding":null,"authors":["Curtis McDonald","Andrew Barron"],"abstract":"Score based approaches to sampling have shown much success as a generative algorithm to produce new samples from a target density given a pool of initial samples. In this work, we consider if we have no initial samples from the target density, but rather $0^{th}$ and $1^{st}$ order oracle access to the log likelihood. Such problems may arise in Bayesian posterior sampling, or in approximate minimization of non-convex functions. Using this knowledge alone, we propose a Monte Carlo method to estimate the score empirically as a particular expectation of a random variable. Using this estimator, we can then run a discrete version of the backward flow SDE to produce samples from the target density. This approach has the benefit of not relying on a pool of initial samples from the target density, and it does not rely on a neural network or other black box model to estimate the score.","url_abs":"https://arxiv.org/abs/2212.03325v1","url_pdf":"https://arxiv.org/pdf/2212.03325v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"proposal-of-a-score-based-approach-to","repo_url":"https://github.com/cmcdonald-1/score_modeling_monte_carlo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2212.03325","atlas_url":"https://app.syntology.ai/?focus=2212.03325","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.03325"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/CMcDonald-1/Score_Modeling_Monte_Carlo","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cmcdonald-1/score_modeling_monte_carlo","reach":{"status":"ok"}}],"summary":{"ran_draft_wrong":1,"unverified":6},"by_repo_kind":{"official":{"samples":7,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":7,"samples":[{"code_sha256_prefix":"35622756743e2538","entry":"est_scores_v3","repo":"cmcdonald-1/score_modeling_monte_carlo","repo_kind":"official","path":"himmelblau_sampler_final.py","file_url":"https://github.com/cmcdonald-1/score_modeling_monte_carlo/blob/HEAD/himmelblau_sampler_final.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"35622756743e2538"}},{"code_sha256_prefix":"68d7b8a433f9bac7","entry":"est_scores_v3","repo":"cmcdonald-1/score_modeling_monte_carlo","repo_kind":"official","path":"himmelblau_sampler_final.py","file_url":"https://github.com/cmcdonald-1/score_modeling_monte_carlo/blob/HEAD/himmelblau_sampler_final.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"68d7b8a433f9bac7"}},{"code_sha256_prefix":"689ad1a896c354e8","entry":"est_scores_v4","repo":"cmcdonald-1/score_modeling_monte_carlo","repo_kind":"official","path":"himmelblau_sampler_final.py","file_url":"https://github.com/cmcdonald-1/score_modeling_monte_carlo/blob/HEAD/himmelblau_sampler_final.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"689ad1a896c354e8"}},{"code_sha256_prefix":"fd8ff40237b65ecd","entry":"f_func","repo":"cmcdonald-1/score_modeling_monte_carlo","repo_kind":"official","path":"basic_neurips_example_final.py","file_url":"https://github.com/cmcdonald-1/score_modeling_monte_carlo/blob/HEAD/basic_neurips_example_final.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"fd8ff40237b65ecd"}},{"code_sha256_prefix":"789dd2f324f9485a","entry":"f_grad_func","repo":"cmcdonald-1/score_modeling_monte_carlo","repo_kind":"official","path":"basic_neurips_example_final.py","file_url":"https://github.com/cmcdonald-1/score_modeling_monte_carlo/blob/HEAD/basic_neurips_example_final.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"789dd2f324f9485a"}},{"code_sha256_prefix":"1f32f9dab7fae92b","entry":"fast_vectorized_f","repo":"cmcdonald-1/score_modeling_monte_carlo","repo_kind":"official","path":"basic_neurips_example_final.py","file_url":"https://github.com/cmcdonald-1/score_modeling_monte_carlo/blob/HEAD/basic_neurips_example_final.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1f32f9dab7fae92b"}},{"code_sha256_prefix":"0678af2a5dc49299","entry":"himmelbau","repo":"cmcdonald-1/score_modeling_monte_carlo","repo_kind":"official","path":"himmelblau_sampler_final.py","file_url":"https://github.com/cmcdonald-1/score_modeling_monte_carlo/blob/HEAD/himmelblau_sampler_final.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0678af2a5dc49299"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}