{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QD4WHNUIWVVJS7NZINCOYU6MWQ","short_pith_number":"pith:QD4WHNUI","canonical_record":{"source":{"id":"1706.04632","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-14T18:44:29Z","cross_cats_sorted":[],"title_canon_sha256":"8b06ecd9f5311ae8f4132389ade5b0e01ec0b0d625b65df68979de5f7c1c5930","abstract_canon_sha256":"e133a237d7af7f5b5ad2aa4368dfc27786296a26f435d149123fa055f228b8c2"},"schema_version":"1.0"},"canonical_sha256":"80f963b688b56a997db94344ec53ccb429557535db396304b6fb44df80dca6ab","source":{"kind":"arxiv","id":"1706.04632","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04632","created_at":"2026-05-18T00:42:19Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04632v1","created_at":"2026-05-18T00:42:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04632","created_at":"2026-05-18T00:42:19Z"},{"alias_kind":"pith_short_12","alias_value":"QD4WHNUIWVVJ","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_16","alias_value":"QD4WHNUIWVVJS7NZ","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_8","alias_value":"QD4WHNUI","created_at":"2026-05-18T12:31:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QD4WHNUIWVVJS7NZINCOYU6MWQ","target":"record","payload":{"canonical_record":{"source":{"id":"1706.04632","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-14T18:44:29Z","cross_cats_sorted":[],"title_canon_sha256":"8b06ecd9f5311ae8f4132389ade5b0e01ec0b0d625b65df68979de5f7c1c5930","abstract_canon_sha256":"e133a237d7af7f5b5ad2aa4368dfc27786296a26f435d149123fa055f228b8c2"},"schema_version":"1.0"},"canonical_sha256":"80f963b688b56a997db94344ec53ccb429557535db396304b6fb44df80dca6ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:19.470712Z","signature_b64":"US4hlC0JGrkD13zPOrnZJZttBo98RnzOHLOgB/h2v6cCRlUBp2xm8yB+esQubaDFdMgKvMZAz6CP0xm9zhFuDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80f963b688b56a997db94344ec53ccb429557535db396304b6fb44df80dca6ab","last_reissued_at":"2026-05-18T00:42:19.470241Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:19.470241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.04632","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"erkPdJcrKe4+y6dvVFnk0s974dXy+qYZGMzf5KvnOgRvQ/anZ9YAfJ2mI9CRcvna7nKkJ15oQeI7fsko082aDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T12:13:21.430609Z"},"content_sha256":"167d192f202da69d6800db1e71597e50b357148355b29dce5912342c1d7e893d","schema_version":"1.0","event_id":"sha256:167d192f202da69d6800db1e71597e50b357148355b29dce5912342c1d7e893d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QD4WHNUIWVVJS7NZINCOYU6MWQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stochastic Gradient MCMC Methods for Hidden Markov Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Emily B. Fox, Nicholas J. Foti, Yi-An Ma","submitted_at":"2017-06-14T18:44:29Z","abstract_excerpt":"Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling Bayesian inference to large datasets under an assumption of i.i.d data. We instead develop an SG-MCMC algorithm to learn the parameters of hidden Markov models (HMMs) for time-dependent data. There are two challenges to applying SG-MCMC in this setting: The latent discrete states, and needing to break dependencies when considering minibatches. We consider a marginal likelihood representation of the HMM and propose an algorithm that harnesses the inherent memory decay of the process. We demonstrate the effectiveness of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04632","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g0q28MP8G2RwjnkVGRNKNUng7vbX0aVAkcwjg9NUXnAdyvcqwBwiIM4Y1Xsrmu0Cg0vtoFJnQFNxdUN/wGXDDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T12:13:21.430947Z"},"content_sha256":"b1af16d96813537494bf6a4a1d10c1a39b087aaa7c60e16c3aa4942229a08c3e","schema_version":"1.0","event_id":"sha256:b1af16d96813537494bf6a4a1d10c1a39b087aaa7c60e16c3aa4942229a08c3e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QD4WHNUIWVVJS7NZINCOYU6MWQ/bundle.json","state_url":"https://pith.science/pith/QD4WHNUIWVVJS7NZINCOYU6MWQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QD4WHNUIWVVJS7NZINCOYU6MWQ/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":"2026-06-27T12:13:21Z","links":{"resolver":"https://pith.science/pith/QD4WHNUIWVVJS7NZINCOYU6MWQ","bundle":"https://pith.science/pith/QD4WHNUIWVVJS7NZINCOYU6MWQ/bundle.json","state":"https://pith.science/pith/QD4WHNUIWVVJS7NZINCOYU6MWQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QD4WHNUIWVVJS7NZINCOYU6MWQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QD4WHNUIWVVJS7NZINCOYU6MWQ","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":"e133a237d7af7f5b5ad2aa4368dfc27786296a26f435d149123fa055f228b8c2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-14T18:44:29Z","title_canon_sha256":"8b06ecd9f5311ae8f4132389ade5b0e01ec0b0d625b65df68979de5f7c1c5930"},"schema_version":"1.0","source":{"id":"1706.04632","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04632","created_at":"2026-05-18T00:42:19Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04632v1","created_at":"2026-05-18T00:42:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04632","created_at":"2026-05-18T00:42:19Z"},{"alias_kind":"pith_short_12","alias_value":"QD4WHNUIWVVJ","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_16","alias_value":"QD4WHNUIWVVJS7NZ","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_8","alias_value":"QD4WHNUI","created_at":"2026-05-18T12:31:37Z"}],"graph_snapshots":[{"event_id":"sha256:b1af16d96813537494bf6a4a1d10c1a39b087aaa7c60e16c3aa4942229a08c3e","target":"graph","created_at":"2026-05-18T00:42:19Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling Bayesian inference to large datasets under an assumption of i.i.d data. We instead develop an SG-MCMC algorithm to learn the parameters of hidden Markov models (HMMs) for time-dependent data. There are two challenges to applying SG-MCMC in this setting: The latent discrete states, and needing to break dependencies when considering minibatches. We consider a marginal likelihood representation of the HMM and propose an algorithm that harnesses the inherent memory decay of the process. We demonstrate the effectiveness of ","authors_text":"Emily B. Fox, Nicholas J. Foti, Yi-An Ma","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-14T18:44:29Z","title":"Stochastic Gradient MCMC Methods for Hidden Markov Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04632","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:167d192f202da69d6800db1e71597e50b357148355b29dce5912342c1d7e893d","target":"record","created_at":"2026-05-18T00:42:19Z","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":"e133a237d7af7f5b5ad2aa4368dfc27786296a26f435d149123fa055f228b8c2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-14T18:44:29Z","title_canon_sha256":"8b06ecd9f5311ae8f4132389ade5b0e01ec0b0d625b65df68979de5f7c1c5930"},"schema_version":"1.0","source":{"id":"1706.04632","kind":"arxiv","version":1}},"canonical_sha256":"80f963b688b56a997db94344ec53ccb429557535db396304b6fb44df80dca6ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"80f963b688b56a997db94344ec53ccb429557535db396304b6fb44df80dca6ab","first_computed_at":"2026-05-18T00:42:19.470241Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:19.470241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"US4hlC0JGrkD13zPOrnZJZttBo98RnzOHLOgB/h2v6cCRlUBp2xm8yB+esQubaDFdMgKvMZAz6CP0xm9zhFuDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:19.470712Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.04632","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:167d192f202da69d6800db1e71597e50b357148355b29dce5912342c1d7e893d","sha256:b1af16d96813537494bf6a4a1d10c1a39b087aaa7c60e16c3aa4942229a08c3e"],"state_sha256":"2a091aa10b708f656a35593f96715d7cb89d4c050fc1ffbf2c623fd991badf54"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"De3KTKR1OJbxKepsev7zjnlyhL9UMp8GkkUMeLLUUGXQRhpGdHV1ofsvfy7ZBm7NuwDeAqb3uZyp+P4oLg/2BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-27T12:13:21.432802Z","bundle_sha256":"ce14e6c5353a067aeab8352f58bae3f79856dd83b5e3902a3014f3813cf5a6e6"}}