{"id":"W2124227444","doi":"","title":"Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models","year":2009,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Graphical model; Inference; Bayesian inference; Computer science; Algorithm; Laplace's method; Markov chain; Bayesian probability; Hidden Markov model; Gaussian; Mathematics; Mathematical optimization; Artificial intelligence; Machine learning","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":[0.0002472895,0.0002716022,0.0002880127,0.0001302099,0.0003608244,0.0005920597,0.001182193,0.0001428031,0.00002121236],"category_scores_gemma":[0.00003058454,0.0002282897,0.0001303061,0.0003892869,0.00003870897,0.001319177,0.0001099572,0.0002481873,0.000008929492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002765661,"about_ca_system_score_gemma":0.000125901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003304378,"about_ca_topic_score_gemma":0.00001552288,"domain_scores_codex":[0.9980165,0.00003352515,0.0004067316,0.0006474216,0.000271968,0.0006238543],"domain_scores_gemma":[0.9987838,0.0001141945,0.00009174769,0.0006023983,0.0001563334,0.0002515119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009216966,0.00003262243,0.00008173312,0.00001025691,0.000009042438,0.000003365475,0.0002933691,0.006290144,0.001248955,0.9481094,0.0004097365,0.04350215],"study_design_scores_gemma":[0.0002198937,0.000146111,0.0002664861,0.00001834376,0.000003050804,0.000007600731,0.000009141489,0.6559037,0.0008329189,0.3423502,0.00001384398,0.0002286797],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009062037,0.00002017578,0.9790353,0.002336669,0.0001778327,0.0002616051,0.000002720742,0.0003269291,0.008776797],"genre_scores_gemma":[0.6682602,0.000003995464,0.3305784,0.0009523851,0.00006754854,0.00001431902,0.000004339863,0.000006981457,0.0001118198],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6591981,"threshold_uncertainty_score":0.9309383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03957967680449925,"score_gpt":0.3027815062369483,"score_spread":0.2632018294324491,"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."}}