{"paper":{"title":"Learning Continuous Time Bayesian Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Christian R. Shelton, Daphne Koller, Uri Nodelman","submitted_at":"2012-10-19T15:07:23Z","abstract_excerpt":"Continuous time Bayesian networks (CTBNs) describe structured stochastic     processes with finitely many states that evolve over continuous time. A CTBN is     a directed (possibly cyclic) dependency graph over a set of variables, each of     which represents a finite state continuous time Markov process whose transition     model is a function of its parents. We address the problem of learning     parameters and structure of a CTBN from fully observed data. We define a     conjugate prior for CTBNs, and show how it can be used both for Bayesian     parameter estimation and as the basis of a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2498","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"}