{"id":"W2408131130","doi":"10.1609/aaai.v26i1.8397","title":"A Search Algorithm for Latent Variable Models with Unbounded Domains","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Latent variable; A priori and a posteriori; Latent variable model; Prior probability; Algorithm; Computer science; Probabilistic logic; Probabilistic latent semantic analysis; Domain (mathematical analysis); Variable (mathematics); Latent Dirichlet allocation; Dirichlet distribution; Mathematics; Machine learning; Artificial intelligence; Topic model; Bayesian probability","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007834139,0.0002333261,0.0003234839,0.00008323884,0.0002668713,0.0004045425,0.001453437,0.0001056356,0.00002241246],"category_scores_gemma":[0.00009345582,0.0001609183,0.0001196028,0.0008340806,0.0001875887,0.000474594,0.0003595623,0.0002766782,0.000007640793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005150064,"about_ca_system_score_gemma":0.0004168904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002853937,"about_ca_topic_score_gemma":0.000005556648,"domain_scores_codex":[0.9979084,0.00003237124,0.0004052953,0.0006630753,0.0005175362,0.0004733561],"domain_scores_gemma":[0.9975973,0.0001324641,0.0001853709,0.0004115778,0.001548617,0.0001246238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003011606,0.0001216057,0.000003864721,0.00003110829,0.00002443948,0.000001042587,0.0005977337,0.0001540932,0.01081796,0.830079,0.00004436661,0.1580947],"study_design_scores_gemma":[0.00003461018,0.0001126735,0.000002024061,0.00009167406,0.000009928841,0.000009374044,0.00006753757,0.3450391,0.232118,0.422359,0.00004178313,0.000114408],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001893599,0.00003040326,0.9877954,0.002177425,0.0002062279,0.0004765541,0.00001163141,0.0000629869,0.007345735],"genre_scores_gemma":[0.2465859,0.00002846286,0.752303,0.0002879483,0.00005610695,0.00005918225,8.094061e-7,0.00001609307,0.0006625237],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.40772,"threshold_uncertainty_score":0.6562058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09569180326638813,"score_gpt":0.3083649686390117,"score_spread":0.2126731653726236,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}