{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:OEDWCNQO2J467CNRDJ2ZHJ6X2M","short_pith_number":"pith:OEDWCNQO","canonical_record":{"source":{"id":"1202.3665","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2012-02-16T20:41:19Z","cross_cats_sorted":["physics.comp-ph","stat.CO"],"title_canon_sha256":"ab88c83ddd28f718facefee3e06386a3bbeea91239cf708d38e2ed10012fdc98","abstract_canon_sha256":"328fde1d9a36f3de908c8d1b810df5e65c3f321166ed7e9fcd4f4ed04bdd52f9"},"schema_version":"1.0"},"canonical_sha256":"710761360ed279ef89b11a7593a7d7d3108cee5e051337eaa6fd80d1b0b1099a","source":{"kind":"arxiv","id":"1202.3665","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1202.3665","created_at":"2026-05-18T04:42:33Z"},{"alias_kind":"arxiv_version","alias_value":"1202.3665v4","created_at":"2026-05-18T04:42:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1202.3665","created_at":"2026-05-18T04:42:33Z"},{"alias_kind":"pith_short_12","alias_value":"OEDWCNQO2J46","created_at":"2026-05-18T12:27:16Z"},{"alias_kind":"pith_short_16","alias_value":"OEDWCNQO2J467CNR","created_at":"2026-05-18T12:27:16Z"},{"alias_kind":"pith_short_8","alias_value":"OEDWCNQO","created_at":"2026-05-18T12:27:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:OEDWCNQO2J467CNRDJ2ZHJ6X2M","target":"record","payload":{"canonical_record":{"source":{"id":"1202.3665","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2012-02-16T20:41:19Z","cross_cats_sorted":["physics.comp-ph","stat.CO"],"title_canon_sha256":"ab88c83ddd28f718facefee3e06386a3bbeea91239cf708d38e2ed10012fdc98","abstract_canon_sha256":"328fde1d9a36f3de908c8d1b810df5e65c3f321166ed7e9fcd4f4ed04bdd52f9"},"schema_version":"1.0"},"canonical_sha256":"710761360ed279ef89b11a7593a7d7d3108cee5e051337eaa6fd80d1b0b1099a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:42:33.582455Z","signature_b64":"O2QQ/FjP0KJ6O2RBl5bGEhapnHnxGECmaAPlGumm3raaAwnYpdw2rv4rbj4YSBJIrMvzI4j//Iofz8z5KbLZAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"710761360ed279ef89b11a7593a7d7d3108cee5e051337eaa6fd80d1b0b1099a","last_reissued_at":"2026-05-18T04:42:33.581603Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:42:33.581603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1202.3665","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:42:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uBaX4/LL6ZIR1auMujmRPnDjAH1KCYESlzEh/NT3hVOqjYx0IOki8s6VG+Yy9jJvyvZkAPsNBd9O9xBs+mLwCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:13:26.455628Z"},"content_sha256":"178d9c8b4d5bedf7a5ab31ba2f2f2de30b7838553344720b6ab8e1d4beaa2746","schema_version":"1.0","event_id":"sha256:178d9c8b4d5bedf7a5ab31ba2f2f2de30b7838553344720b6ab8e1d4beaa2746"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:OEDWCNQO2J467CNRDJ2ZHJ6X2M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"emcee: The MCMC Hammer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"The emcee package implements an affine-invariant ensemble sampler for MCMC that requires tuning only one or two parameters.","cross_cats":["physics.comp-ph","stat.CO"],"primary_cat":"astro-ph.IM","authors_text":"Daniel Foreman-Mackey, David W. Hogg, Dustin Lang, Jonathan Goodman","submitted_at":"2012-02-16T20:41:19Z","abstract_excerpt":"We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to $\\sim"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ∼N² for a traditional algorithm in an N-dimensional parameter space.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The ensemble sampler converges reliably to the target posterior for the user's specific likelihood function and that the computational cost of evaluating the likelihood many times is feasible.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"emcee delivers a stable Python implementation of the affine-invariant ensemble MCMC algorithm that requires minimal hand-tuning and supports easy parallelization.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"The emcee package implements an affine-invariant ensemble sampler for MCMC that requires tuning only one or two parameters.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e94c257ff6e36fc123913ee7ae00e7bb6f17a73024f8db7eceb27addd82f5581"},"source":{"id":"1202.3665","kind":"arxiv","version":4},"verdict":{"id":"7553479d-2964-4703-9b07-4e9ba4e90ad4","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T21:55:44.300195Z","strongest_claim":"One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ∼N² for a traditional algorithm in an N-dimensional parameter space.","one_line_summary":"emcee delivers a stable Python implementation of the affine-invariant ensemble MCMC algorithm that requires minimal hand-tuning and supports easy parallelization.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The ensemble sampler converges reliably to the target posterior for the user's specific likelihood function and that the computational cost of evaluating the likelihood many times is feasible.","pith_extraction_headline":"The emcee package implements an affine-invariant ensemble sampler for MCMC that requires tuning only one or two parameters."},"references":{"count":28,"sample":[{"doi":"","year":null,"title":"Akeret, J., Seehars, S., Amara, A., Refregier, A., & Csillaghy, A. 201 2, arXiv:1212.1721","work_id":"925cf44b-e9fa-4e00-9d44-4ab1705971f7","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2012,"title":"2012, ApJ, 753, 148","work_id":"7d9c69fc-5bcd-4259-b9ef-3c1ac825c712","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2012,"title":"Bovy, J., Rix, H.-W., Hogg, D. W., et al. 2012, ApJ, 755, 115 – 13 –","work_id":"da5ba2da-397e-46d4-8979-3a6528417b41","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2012,"title":"Bovy, J., Allende Prieto, C., Beers, T. C., et al. 2012, ApJ, 759, 131","work_id":"7f4a4068-cf90-4a71-92c4-6e8305af83f4","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2012,"title":"B., S´ anchez-Janssen, R., Labb´ e, I., et al","work_id":"462986e6-3e68-45f8-973f-cdf41eddd1c3","ref_index":5,"cited_arxiv_id":"0912.2380","is_internal_anchor":false}],"resolved_work":28,"snapshot_sha256":"1e4d0a4a55befa5ddaaa469a8e05d9a619a781a24a3dcaee86511ddfb9f5f0bd","internal_anchors":1},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6889c4b275eafb33997f06122442f3247e9d94dc920a1ca1960446f52b346fcc"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"7553479d-2964-4703-9b07-4e9ba4e90ad4"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:42:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"INIjYy8Wm+fvYhf4zmtac/pTUEM1PsUhCJFRWx5iVEYkO6kf8HtS5WZnr+3zZTmSPOuVvBJiPwF8Gyd40ZPqDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:13:26.456881Z"},"content_sha256":"934c0a5b01ad6ec5bc5570b997139ed498ec23f533e848a6e6a763c3f64cef0b","schema_version":"1.0","event_id":"sha256:934c0a5b01ad6ec5bc5570b997139ed498ec23f533e848a6e6a763c3f64cef0b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OEDWCNQO2J467CNRDJ2ZHJ6X2M/bundle.json","state_url":"https://pith.science/pith/OEDWCNQO2J467CNRDJ2ZHJ6X2M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OEDWCNQO2J467CNRDJ2ZHJ6X2M/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T22:13:26Z","links":{"resolver":"https://pith.science/pith/OEDWCNQO2J467CNRDJ2ZHJ6X2M","bundle":"https://pith.science/pith/OEDWCNQO2J467CNRDJ2ZHJ6X2M/bundle.json","state":"https://pith.science/pith/OEDWCNQO2J467CNRDJ2ZHJ6X2M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OEDWCNQO2J467CNRDJ2ZHJ6X2M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:OEDWCNQO2J467CNRDJ2ZHJ6X2M","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"328fde1d9a36f3de908c8d1b810df5e65c3f321166ed7e9fcd4f4ed04bdd52f9","cross_cats_sorted":["physics.comp-ph","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2012-02-16T20:41:19Z","title_canon_sha256":"ab88c83ddd28f718facefee3e06386a3bbeea91239cf708d38e2ed10012fdc98"},"schema_version":"1.0","source":{"id":"1202.3665","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1202.3665","created_at":"2026-05-18T04:42:33Z"},{"alias_kind":"arxiv_version","alias_value":"1202.3665v4","created_at":"2026-05-18T04:42:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1202.3665","created_at":"2026-05-18T04:42:33Z"},{"alias_kind":"pith_short_12","alias_value":"OEDWCNQO2J46","created_at":"2026-05-18T12:27:16Z"},{"alias_kind":"pith_short_16","alias_value":"OEDWCNQO2J467CNR","created_at":"2026-05-18T12:27:16Z"},{"alias_kind":"pith_short_8","alias_value":"OEDWCNQO","created_at":"2026-05-18T12:27:16Z"}],"graph_snapshots":[{"event_id":"sha256:934c0a5b01ad6ec5bc5570b997139ed498ec23f533e848a6e6a763c3f64cef0b","target":"graph","created_at":"2026-05-18T04:42:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ∼N² for a traditional algorithm in an N-dimensional parameter space."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The ensemble sampler converges reliably to the target posterior for the user's specific likelihood function and that the computational cost of evaluating the likelihood many times is feasible."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"emcee delivers a stable Python implementation of the affine-invariant ensemble MCMC algorithm that requires minimal hand-tuning and supports easy parallelization."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"The emcee package implements an affine-invariant ensemble sampler for MCMC that requires tuning only one or two parameters."}],"snapshot_sha256":"e94c257ff6e36fc123913ee7ae00e7bb6f17a73024f8db7eceb27addd82f5581"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6889c4b275eafb33997f06122442f3247e9d94dc920a1ca1960446f52b346fcc"},"paper":{"abstract_excerpt":"We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to $\\sim","authors_text":"Daniel Foreman-Mackey, David W. Hogg, Dustin Lang, Jonathan Goodman","cross_cats":["physics.comp-ph","stat.CO"],"headline":"The emcee package implements an affine-invariant ensemble sampler for MCMC that requires tuning only one or two parameters.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2012-02-16T20:41:19Z","title":"emcee: The MCMC Hammer"},"references":{"count":28,"internal_anchors":1,"resolved_work":28,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Akeret, J., Seehars, S., Amara, A., Refregier, A., & Csillaghy, A. 201 2, arXiv:1212.1721","work_id":"925cf44b-e9fa-4e00-9d44-4ab1705971f7","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"2012, ApJ, 753, 148","work_id":"7d9c69fc-5bcd-4259-b9ef-3c1ac825c712","year":2012},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Bovy, J., Rix, H.-W., Hogg, D. W., et al. 2012, ApJ, 755, 115 – 13 –","work_id":"da5ba2da-397e-46d4-8979-3a6528417b41","year":2012},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Bovy, J., Allende Prieto, C., Beers, T. C., et al. 2012, ApJ, 759, 131","work_id":"7f4a4068-cf90-4a71-92c4-6e8305af83f4","year":2012},{"cited_arxiv_id":"0912.2380","doi":"","is_internal_anchor":false,"ref_index":5,"title":"B., S´ anchez-Janssen, R., Labb´ e, I., et al","work_id":"462986e6-3e68-45f8-973f-cdf41eddd1c3","year":2012}],"snapshot_sha256":"1e4d0a4a55befa5ddaaa469a8e05d9a619a781a24a3dcaee86511ddfb9f5f0bd"},"source":{"id":"1202.3665","kind":"arxiv","version":4},"verdict":{"created_at":"2026-05-13T21:55:44.300195Z","id":"7553479d-2964-4703-9b07-4e9ba4e90ad4","model_set":{"reader":"grok-4.3"},"one_line_summary":"emcee delivers a stable Python implementation of the affine-invariant ensemble MCMC algorithm that requires minimal hand-tuning and supports easy parallelization.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"The emcee package implements an affine-invariant ensemble sampler for MCMC that requires tuning only one or two parameters.","strongest_claim":"One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ∼N² for a traditional algorithm in an N-dimensional parameter space.","weakest_assumption":"The ensemble sampler converges reliably to the target posterior for the user's specific likelihood function and that the computational cost of evaluating the likelihood many times is feasible."}},"verdict_id":"7553479d-2964-4703-9b07-4e9ba4e90ad4"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:178d9c8b4d5bedf7a5ab31ba2f2f2de30b7838553344720b6ab8e1d4beaa2746","target":"record","created_at":"2026-05-18T04:42:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"328fde1d9a36f3de908c8d1b810df5e65c3f321166ed7e9fcd4f4ed04bdd52f9","cross_cats_sorted":["physics.comp-ph","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2012-02-16T20:41:19Z","title_canon_sha256":"ab88c83ddd28f718facefee3e06386a3bbeea91239cf708d38e2ed10012fdc98"},"schema_version":"1.0","source":{"id":"1202.3665","kind":"arxiv","version":4}},"canonical_sha256":"710761360ed279ef89b11a7593a7d7d3108cee5e051337eaa6fd80d1b0b1099a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"710761360ed279ef89b11a7593a7d7d3108cee5e051337eaa6fd80d1b0b1099a","first_computed_at":"2026-05-18T04:42:33.581603Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:42:33.581603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O2QQ/FjP0KJ6O2RBl5bGEhapnHnxGECmaAPlGumm3raaAwnYpdw2rv4rbj4YSBJIrMvzI4j//Iofz8z5KbLZAg==","signature_status":"signed_v1","signed_at":"2026-05-18T04:42:33.582455Z","signed_message":"canonical_sha256_bytes"},"source_id":"1202.3665","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:178d9c8b4d5bedf7a5ab31ba2f2f2de30b7838553344720b6ab8e1d4beaa2746","sha256:934c0a5b01ad6ec5bc5570b997139ed498ec23f533e848a6e6a763c3f64cef0b"],"state_sha256":"51fde8c3c7004b4a802dcd8267354fb2d4e5168073e201bd7f2adde636235dd6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n9gdTy0eRg+KdAH2omqYof/RoEy/+FUC04mBlr+p3+Dy4wjOZ9YNzbg1fBUu6QlebEJYoRM8VLBczQ2k8bE3BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T22:13:26.462349Z","bundle_sha256":"a07870d187128decd6c726288e2810804f63dce77d9f30d89a00733b6c444b11"}}