{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:ZFQMO2IWODFOFS2ERE3VYEFN7R","short_pith_number":"pith:ZFQMO2IW","canonical_record":{"source":{"id":"2109.15134","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-09-30T13:55:16Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f9ad6ff3eedab6c6b14258a501ed1002e4faac853ed24bc4ddc263fe49ff30fa","abstract_canon_sha256":"4230f0ebeca8d7c97f88d754d49cfb2a6e5f99e6b8766d62a35e702e37de8691"},"schema_version":"1.0"},"canonical_sha256":"c960c7691670cae2cb4489375c10adfc66bfa569659befe2d73e0fe76cf4d874","source":{"kind":"arxiv","id":"2109.15134","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.15134","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"arxiv_version","alias_value":"2109.15134v3","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.15134","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"pith_short_12","alias_value":"ZFQMO2IWODFO","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"pith_short_16","alias_value":"ZFQMO2IWODFOFS2E","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"pith_short_8","alias_value":"ZFQMO2IW","created_at":"2026-07-05T04:05:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:ZFQMO2IWODFOFS2ERE3VYEFN7R","target":"record","payload":{"canonical_record":{"source":{"id":"2109.15134","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-09-30T13:55:16Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f9ad6ff3eedab6c6b14258a501ed1002e4faac853ed24bc4ddc263fe49ff30fa","abstract_canon_sha256":"4230f0ebeca8d7c97f88d754d49cfb2a6e5f99e6b8766d62a35e702e37de8691"},"schema_version":"1.0"},"canonical_sha256":"c960c7691670cae2cb4489375c10adfc66bfa569659befe2d73e0fe76cf4d874","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:05:13.361039Z","signature_b64":"x88s7BN2j1ZsxJfSU6VrrSnyOOeP/5DcFnO2WL4yPyAYhQ4r8OEEw/MzUKy/om9CAbrAuRziHL3fgg8ef02OCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c960c7691670cae2cb4489375c10adfc66bfa569659befe2d73e0fe76cf4d874","last_reissued_at":"2026-07-05T04:05:13.360645Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:05:13.360645Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.15134","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:05:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PCe+XWasvTgM/RLGGU+1GByCp4xaa4DbVEH+8coqmae9cbZkOfCaX+jTTsxixmnlZ+jhakekr4hmFH7xR7CzBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T05:39:49.468264Z"},"content_sha256":"5af5f70c3d358ba0880449064477a69443b2bd46aead0a6bca3bd15c2d96f047","schema_version":"1.0","event_id":"sha256:5af5f70c3d358ba0880449064477a69443b2bd46aead0a6bca3bd15c2d96f047"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:ZFQMO2IWODFOFS2ERE3VYEFN7R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variational Marginal Particle Filters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Daniel Sheldon, Jinlin Lai, Justin Domke","submitted_at":"2021-09-30T13:55:16Z","abstract_excerpt":"Variational inference for state space models (SSMs) is known to be hard in general. Recent works focus on deriving variational objectives for SSMs from unbiased sequential Monte Carlo estimators. We reveal that the marginal particle filter is obtained from sequential Monte Carlo by applying Rao-Blackwellization operations, which sacrifices the trajectory information for reduced variance and differentiability. We propose the variational marginal particle filter (VMPF), which is a differentiable and reparameterizable variational filtering objective for SSMs based on an unbiased estimator. We fin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.15134","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.15134/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T04:05:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3R2HF37Hjsx/eSyC1mY4SqnQZn6ueEjXa6gjo1raHpEmth90LVI8FPxHUVLgKzrY+yvRG7t3a47H4kWQS1tcCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T05:39:49.468863Z"},"content_sha256":"487371fc8c2359e0e2debaf0ab52c0424df145fea7b34d9e1a9c0a2a27a3a319","schema_version":"1.0","event_id":"sha256:487371fc8c2359e0e2debaf0ab52c0424df145fea7b34d9e1a9c0a2a27a3a319"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZFQMO2IWODFOFS2ERE3VYEFN7R/bundle.json","state_url":"https://pith.science/pith/ZFQMO2IWODFOFS2ERE3VYEFN7R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZFQMO2IWODFOFS2ERE3VYEFN7R/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":"2026-08-19T05:39:49Z","links":{"resolver":"https://pith.science/pith/ZFQMO2IWODFOFS2ERE3VYEFN7R","bundle":"https://pith.science/pith/ZFQMO2IWODFOFS2ERE3VYEFN7R/bundle.json","state":"https://pith.science/pith/ZFQMO2IWODFOFS2ERE3VYEFN7R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZFQMO2IWODFOFS2ERE3VYEFN7R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:ZFQMO2IWODFOFS2ERE3VYEFN7R","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":"4230f0ebeca8d7c97f88d754d49cfb2a6e5f99e6b8766d62a35e702e37de8691","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-09-30T13:55:16Z","title_canon_sha256":"f9ad6ff3eedab6c6b14258a501ed1002e4faac853ed24bc4ddc263fe49ff30fa"},"schema_version":"1.0","source":{"id":"2109.15134","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.15134","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"arxiv_version","alias_value":"2109.15134v3","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.15134","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"pith_short_12","alias_value":"ZFQMO2IWODFO","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"pith_short_16","alias_value":"ZFQMO2IWODFOFS2E","created_at":"2026-07-05T04:05:13Z"},{"alias_kind":"pith_short_8","alias_value":"ZFQMO2IW","created_at":"2026-07-05T04:05:13Z"}],"graph_snapshots":[{"event_id":"sha256:487371fc8c2359e0e2debaf0ab52c0424df145fea7b34d9e1a9c0a2a27a3a319","target":"graph","created_at":"2026-07-05T04:05:13Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2109.15134/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Variational inference for state space models (SSMs) is known to be hard in general. Recent works focus on deriving variational objectives for SSMs from unbiased sequential Monte Carlo estimators. We reveal that the marginal particle filter is obtained from sequential Monte Carlo by applying Rao-Blackwellization operations, which sacrifices the trajectory information for reduced variance and differentiability. We propose the variational marginal particle filter (VMPF), which is a differentiable and reparameterizable variational filtering objective for SSMs based on an unbiased estimator. We fin","authors_text":"Daniel Sheldon, Jinlin Lai, Justin Domke","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-09-30T13:55:16Z","title":"Variational Marginal Particle Filters"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.15134","kind":"arxiv","version":3},"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":"sha256:5af5f70c3d358ba0880449064477a69443b2bd46aead0a6bca3bd15c2d96f047","target":"record","created_at":"2026-07-05T04:05:13Z","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":"4230f0ebeca8d7c97f88d754d49cfb2a6e5f99e6b8766d62a35e702e37de8691","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-09-30T13:55:16Z","title_canon_sha256":"f9ad6ff3eedab6c6b14258a501ed1002e4faac853ed24bc4ddc263fe49ff30fa"},"schema_version":"1.0","source":{"id":"2109.15134","kind":"arxiv","version":3}},"canonical_sha256":"c960c7691670cae2cb4489375c10adfc66bfa569659befe2d73e0fe76cf4d874","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c960c7691670cae2cb4489375c10adfc66bfa569659befe2d73e0fe76cf4d874","first_computed_at":"2026-07-05T04:05:13.360645Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:05:13.360645Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x88s7BN2j1ZsxJfSU6VrrSnyOOeP/5DcFnO2WL4yPyAYhQ4r8OEEw/MzUKy/om9CAbrAuRziHL3fgg8ef02OCg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:05:13.361039Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.15134","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5af5f70c3d358ba0880449064477a69443b2bd46aead0a6bca3bd15c2d96f047","sha256:487371fc8c2359e0e2debaf0ab52c0424df145fea7b34d9e1a9c0a2a27a3a319"],"state_sha256":"393ebf839adcaf07cc20e3fddb1b7eafaabfbe19b6da07a53b4f1b5844d07693"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KcA+ntya7AGgxNYf/M/lCix8GIz+pqyHyNbPKXbFoexQbTX9hJRKR/KizE4trslvpTb3ADP4iX/dfyLzWPQRCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T05:39:49.474084Z","bundle_sha256":"8235c7d44f258e545acf258106cd33dad36b06ec644fc6d706ddfe264639d530"}}