{"paper":{"title":"On Triangulating Dynamic Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chris Bartels, Jeff A. Bilmes","submitted_at":"2012-10-19T15:03:47Z","abstract_excerpt":"This paper introduces new methodology to triangulate dynamic Bayesian     networks (DBNs) and dynamic graphical models (DGMs). While most methods to     triangulate such networks use some form of constrained elimination scheme based     on properties of the underlying directed graph, we find it useful to view     triangulation and elimination using properties only of the resulting undirected     graph, obtained after the moralization step. We first briefly introduce the     Graphical model toolkit (GMTK) and its notion of dynamic graphical models, one     that slightly extends the standard not"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2448","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"}