{"paper":{"title":"Variational Probabilistic Inference and the QMR-DT Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"M. I. Jordan, T. S. Jaakkola","submitted_at":"2011-05-27T01:53:36Z","abstract_excerpt":"We describe a variational approximation method for efficient    inference in large-scale probabilistic models.  Variational methods    are deterministic procedures that provide approximations to marginal    and conditional probabilities of interest.  They provide alternatives    to approximate inference methods based on stochastic sampling or    search.  We describe a variational approach to the problem of    diagnostic inference in the `Quick Medical Reference' (QMR) network.    The QMR network is a large-scale probabilistic graphical model built    on statistical and expert knowledge.  Exact"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1105.5462","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"}