{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:3OG22FTGHKN22TDCKGP5EQO4XF","short_pith_number":"pith:3OG22FTG","canonical_record":{"source":{"id":"1903.10860","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2019-03-26T13:25:50Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"1d26192cdffa7ffbee51e8ee90f8541ed1e93cfddad028b271a4e0b21df8e53d","abstract_canon_sha256":"5a73e90ccf2d3706c960a675e35facdbbc02ccb5ce887b8c040d79e942f9539e"},"schema_version":"1.0"},"canonical_sha256":"db8dad16663a9bad4c62519fd241dcb972db9ae27e47c851764a7f8263cd1083","source":{"kind":"arxiv","id":"1903.10860","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.10860","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"arxiv_version","alias_value":"1903.10860v1","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.10860","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"pith_short_12","alias_value":"3OG22FTGHKN2","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"pith_short_16","alias_value":"3OG22FTGHKN22TDC","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"pith_short_8","alias_value":"3OG22FTG","created_at":"2026-07-05T01:07:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:3OG22FTGHKN22TDCKGP5EQO4XF","target":"record","payload":{"canonical_record":{"source":{"id":"1903.10860","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2019-03-26T13:25:50Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"1d26192cdffa7ffbee51e8ee90f8541ed1e93cfddad028b271a4e0b21df8e53d","abstract_canon_sha256":"5a73e90ccf2d3706c960a675e35facdbbc02ccb5ce887b8c040d79e942f9539e"},"schema_version":"1.0"},"canonical_sha256":"db8dad16663a9bad4c62519fd241dcb972db9ae27e47c851764a7f8263cd1083","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:07:24.748820Z","signature_b64":"fYeRWfFWKc5176ayflsHbdhKJ/UyAumtd+hFo130vOLri/tDDy/mkz4Y3j9njgjHNUVkdEFEbHa1rZllT1ZuDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db8dad16663a9bad4c62519fd241dcb972db9ae27e47c851764a7f8263cd1083","last_reissued_at":"2026-07-05T01:07:24.748265Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:07:24.748265Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.10860","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":"2026-07-05T01:07:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q98iWtGOZnxxQZCr+jC/vVCqrEe8xc9c0iAczExa7R44x2Ouh2LULtn4WCw4mng9dW316cMvfBb2G195gaVlBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T02:44:50.730323Z"},"content_sha256":"96acc7cee420e8dca97627fc6e1686a21e58287e77d016ec5ecd296dcb349289","schema_version":"1.0","event_id":"sha256:96acc7cee420e8dca97627fc6e1686a21e58287e77d016ec5ecd296dcb349289"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:3OG22FTGHKN22TDCKGP5EQO4XF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerated Bayesian inference using deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"astro-ph.CO","authors_text":"Adam Moss","submitted_at":"2019-03-26T13:25:50Z","abstract_excerpt":"We present a novel Bayesian inference tool that uses a neural network to parameterise efficient Markov Chain Monte-Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of non-linear, invertible, and non-volume preserving flows. Neural networks are extremely expressive, and can transform complex targets to a simple latent representation. Efficient proposals can then be made in this space, and we demonstrate a high degree of mixing on several challenging distributions. Parameter space can naturally be split into a block diagonal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.10860","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1903.10860/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":"2026-07-05T01:07:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EZ9PCWchjbX7GbXs0fP6MQZmXt6xzjWkgMjwlwE54G3nrdZdURKJajeoLden06qDTKI9FR+5XX/vhf/YYk8jDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T02:44:50.730987Z"},"content_sha256":"e5a01fa6de1038265c1ff3d13dc5d888e6721b35fcbab720e2d1c83fb78565e1","schema_version":"1.0","event_id":"sha256:e5a01fa6de1038265c1ff3d13dc5d888e6721b35fcbab720e2d1c83fb78565e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3OG22FTGHKN22TDCKGP5EQO4XF/bundle.json","state_url":"https://pith.science/pith/3OG22FTGHKN22TDCKGP5EQO4XF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3OG22FTGHKN22TDCKGP5EQO4XF/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":"2026-08-20T02:44:50Z","links":{"resolver":"https://pith.science/pith/3OG22FTGHKN22TDCKGP5EQO4XF","bundle":"https://pith.science/pith/3OG22FTGHKN22TDCKGP5EQO4XF/bundle.json","state":"https://pith.science/pith/3OG22FTGHKN22TDCKGP5EQO4XF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3OG22FTGHKN22TDCKGP5EQO4XF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3OG22FTGHKN22TDCKGP5EQO4XF","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":"5a73e90ccf2d3706c960a675e35facdbbc02ccb5ce887b8c040d79e942f9539e","cross_cats_sorted":["astro-ph.IM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2019-03-26T13:25:50Z","title_canon_sha256":"1d26192cdffa7ffbee51e8ee90f8541ed1e93cfddad028b271a4e0b21df8e53d"},"schema_version":"1.0","source":{"id":"1903.10860","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.10860","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"arxiv_version","alias_value":"1903.10860v1","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.10860","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"pith_short_12","alias_value":"3OG22FTGHKN2","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"pith_short_16","alias_value":"3OG22FTGHKN22TDC","created_at":"2026-07-05T01:07:24Z"},{"alias_kind":"pith_short_8","alias_value":"3OG22FTG","created_at":"2026-07-05T01:07:24Z"}],"graph_snapshots":[{"event_id":"sha256:e5a01fa6de1038265c1ff3d13dc5d888e6721b35fcbab720e2d1c83fb78565e1","target":"graph","created_at":"2026-07-05T01:07:24Z","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/1903.10860/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a novel Bayesian inference tool that uses a neural network to parameterise efficient Markov Chain Monte-Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of non-linear, invertible, and non-volume preserving flows. Neural networks are extremely expressive, and can transform complex targets to a simple latent representation. Efficient proposals can then be made in this space, and we demonstrate a high degree of mixing on several challenging distributions. Parameter space can naturally be split into a block diagonal","authors_text":"Adam Moss","cross_cats":["astro-ph.IM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2019-03-26T13:25:50Z","title":"Accelerated Bayesian inference using deep learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.10860","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":"sha256:96acc7cee420e8dca97627fc6e1686a21e58287e77d016ec5ecd296dcb349289","target":"record","created_at":"2026-07-05T01:07:24Z","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":"5a73e90ccf2d3706c960a675e35facdbbc02ccb5ce887b8c040d79e942f9539e","cross_cats_sorted":["astro-ph.IM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2019-03-26T13:25:50Z","title_canon_sha256":"1d26192cdffa7ffbee51e8ee90f8541ed1e93cfddad028b271a4e0b21df8e53d"},"schema_version":"1.0","source":{"id":"1903.10860","kind":"arxiv","version":1}},"canonical_sha256":"db8dad16663a9bad4c62519fd241dcb972db9ae27e47c851764a7f8263cd1083","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db8dad16663a9bad4c62519fd241dcb972db9ae27e47c851764a7f8263cd1083","first_computed_at":"2026-07-05T01:07:24.748265Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:07:24.748265Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fYeRWfFWKc5176ayflsHbdhKJ/UyAumtd+hFo130vOLri/tDDy/mkz4Y3j9njgjHNUVkdEFEbHa1rZllT1ZuDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:07:24.748820Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.10860","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:96acc7cee420e8dca97627fc6e1686a21e58287e77d016ec5ecd296dcb349289","sha256:e5a01fa6de1038265c1ff3d13dc5d888e6721b35fcbab720e2d1c83fb78565e1"],"state_sha256":"7085bc03817575cc3f140f977d7eb2bdad2c17771445da1e6ae0dfbe98ebf6af"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vFrQ1FxvbIuvkAFJrUD54bLGkUkaca6xBf9/F4JuiFC2PfYQsaQf7IpLsTrCXxlSy6YYLBvkTPoUoOO6DTmtBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T02:44:50.734988Z","bundle_sha256":"87d46f25f47a619549683f7f0685f312da95b11c6d9234bd749c4a1aedac5d37"}}