{"paper":{"title":"Importance Nested Sampling and the MultiNest Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Importance nested sampling reuses all MultiNest points to estimate Bayesian evidence up to ten times more accurately than standard nested sampling.","cross_cats":["physics.data-an","stat.CO"],"primary_cat":"astro-ph.IM","authors_text":"A.N. Pettitt, E. Cameron, F. Feroz, M.P. Hobson","submitted_at":"2013-06-10T09:22:32Z","abstract_excerpt":"Bayesian inference involves two main computational challenges. First, in estimating the parameters of some model for the data, the posterior distribution may well be highly multi-modal: a regime in which the convergence to stationarity of traditional Markov Chain Monte Carlo (MCMC) techniques becomes incredibly slow. Second, in selecting between a set of competing models the necessary estimation of the Bayesian evidence for each is, by definition, a (possibly high-dimensional) integration over the entire parameter space; again this can be a daunting computational task, although new Monte Carlo"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"importance nested sampling (INS), an alternative summation of the MultiNest draws, which can calculate the Bayesian evidence at up to an order of magnitude higher accuracy than `vanilla' NS with no change in the way MultiNest explores the parameter space.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the full set of points collected during MultiNest's constrained likelihood sampling, including those previously discarded, can be treated as an unbiased pseudo-importance sample for evidence summation.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Importance nested sampling re-uses all MultiNest points, including those previously discarded, as a pseudo-importance sample to estimate Bayesian evidence with substantially higher accuracy than vanilla nested sampling.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Importance nested sampling reuses all MultiNest points to estimate Bayesian evidence up to ten times more accurately than standard nested sampling.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9a6fc6aa7a4a358074e66b8a65c76bd03b893d1b5426a00ebabe4c78cc60f3ca"},"source":{"id":"1306.2144","kind":"arxiv","version":3},"verdict":{"id":"0f55abaa-56b0-4a15-a87b-ab60ec5a906e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T20:34:47.957823Z","strongest_claim":"importance nested sampling (INS), an alternative summation of the MultiNest draws, which can calculate the Bayesian evidence at up to an order of magnitude higher accuracy than `vanilla' NS with no change in the way MultiNest explores the parameter space.","one_line_summary":"Importance nested sampling re-uses all MultiNest points, including those previously discarded, as a pseudo-importance sample to estimate Bayesian evidence with substantially higher accuracy than vanilla nested sampling.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the full set of points collected during MultiNest's constrained likelihood sampling, including those previously discarded, can be treated as an unbiased pseudo-importance sample for evidence summation.","pith_extraction_headline":"Importance nested sampling reuses all MultiNest points to estimate Bayesian evidence up to ten times more accurately than standard nested sampling."},"references":{"count":7,"sample":[{"doi":"","year":2008,"title":"Initializing adaptive importance sampling with Markov chains","work_id":"48798e01-fb0b-4c3a-8438-1ad72f6e76b9","ref_index":1,"cited_arxiv_id":"1304.7808","is_internal_anchor":true},{"doi":"","year":2012,"title":"Cluster detection in weak lensing surveys","work_id":"113d163b-7d0c-4cbc-b7df-10dfc7a125d1","ref_index":2,"cited_arxiv_id":"0810.0781","is_internal_anchor":true},{"doi":"","year":2003,"title":"Consistency of the Adaptive Multiple Importance Sampling","work_id":"ed495f11-c129-4604-97be-5b36c80fc979","ref_index":3,"cited_arxiv_id":"1211.2548","is_internal_anchor":true},{"doi":"","year":2012,"title":"Theory of binless multi-state free energy estimation with applications to protein-ligand binding,","work_id":"215dee00-df1c-4b94-9e02-c4129c9bf5f5","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2008,"title":"Bayes in the sky: Bayesian inference and model selection in cosmology,","work_id":"26526550-27cc-4bdf-9aae-764e738fe0c3","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":7,"snapshot_sha256":"66512ad15cd26f173e9c2003f314b515897e3b3b1ad078c9f3d3a0edbae62144","internal_anchors":4},"formal_canon":{"evidence_count":2,"snapshot_sha256":"ea84a3a2357ef8612d1fa79a02eb6989019e269b80e2be3e00eb761d2e6a8254"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}