{"paper":{"title":"Cobaya: Code for Bayesian Analysis of hierarchical physical models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Cobaya automatically detects interdependencies in model pipelines to cache results and block parameters by dependency and cost for faster sampling.","cross_cats":["astro-ph.CO"],"primary_cat":"astro-ph.IM","authors_text":"Antony Lewis, Jesus Torrado","submitted_at":"2020-05-11T17:49:03Z","abstract_excerpt":"We present Cobaya, a general-purpose Bayesian analysis code aimed at models with complex internal interdependencies. Without the need for specific code by the user, interdependencies between different stages of a model pipeline are exploited for sampling efficiency: intermediate results are automatically cached, and parameters are grouped in blocks according to their dependencies and optimally sorted, taking into account their individual computational costs, so as to minimize the cost of their variation during sampling, thanks to a novel algorithm. Cobaya allows exploration of posteriors using"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"parameters are grouped in blocks according to their dependencies and optimally sorted, taking into account their individual computational costs, so as to minimize the cost of their variation during sampling, thanks to a novel algorithm.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the internal structure of an arbitrary user-supplied model pipeline can be automatically inspected and exploited for caching and blocking without requiring user-written dependency declarations or custom code.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Cobaya is a modular Bayesian analysis code that exploits model interdependencies via automatic caching and a novel parameter-blocking algorithm to minimize sampling cost.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Cobaya automatically detects interdependencies in model pipelines to cache results and block parameters by dependency and cost for faster sampling.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"718396e6bd48809f889eeff0c484f01464fe496cc43a3749481982db5b3ab0f5"},"source":{"id":"2005.05290","kind":"arxiv","version":2},"verdict":{"id":"2bcdf20e-55af-4def-80fb-617697388777","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T21:53:33.759262Z","strongest_claim":"parameters are grouped in blocks according to their dependencies and optimally sorted, taking into account their individual computational costs, so as to minimize the cost of their variation during sampling, thanks to a novel algorithm.","one_line_summary":"Cobaya is a modular Bayesian analysis code that exploits model interdependencies via automatic caching and a novel parameter-blocking algorithm to minimize sampling cost.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the internal structure of an arbitrary user-supplied model pipeline can be automatically inspected and exploited for caching and blocking without requiring user-written dependency declarations or custom code.","pith_extraction_headline":"Cobaya automatically detects interdependencies in model pipelines to cache results and block parameters by dependency and cost for faster sampling."},"references":{"count":42,"sample":[{"doi":"","year":null,"title":"cobaya-doc mcmc","work_id":"6be9fd99-d3ed-432b-b130-e32ae739bdcc","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"On their way out of the model (i.e","work_id":"f32739f1-e795-4896-bbda-64f445620798","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"They are inter- faced directly from the 1D continuous distributions of scipy.stats3","work_id":"0a2cd6fc-42c5-416f-8cd7-ec4c4c52b2b2","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"lambda x , y : stats . norm . logpdf ( x - y , loc =0 , scale =0.3)","work_id":"a90d6b15-22d9-4290-91f3-f9af0aae3198","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2005,"title":"R. 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