{"id":"W4319453058","doi":"10.48550/arxiv.2302.02522","title":"Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Markov chain Monte Carlo; Initialization; Inference; Computer science; Bayesian inference; Markov chain; Artificial neural network; Artificial intelligence; Posterior probability; Algorithm; Bayesian probability; Mathematical optimization; Machine learning; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006344604,0.001167197,0.001605832,0.001661653,0.0009479681,0.001812086,0.00284357,0.003058897,0.003162249],"category_scores_gemma":[0.02611357,0.001672897,0.001378249,0.002156997,0.002922039,0.003650223,0.00268776,0.005031693,0.000766231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00264816,"about_ca_system_score_gemma":0.001961601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01338858,"about_ca_topic_score_gemma":0.008340269,"domain_scores_codex":[0.9971091,0.001918313,0.0001002177,0.0003609012,0.0004182656,0.0000931875],"domain_scores_gemma":[0.9925458,0.006292131,0.0002580357,0.0003673738,0.0004222011,0.0001145164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004094112,0.00003413455,0.0006672213,0.0001994123,0.00008737145,0.00005979744,0.0001658808,0.6422517,0.0005635272,0.299061,0.001762672,0.05510637],"study_design_scores_gemma":[0.000008414085,0.000006096111,0.00008870749,0.00003001152,0.000007452952,0.00001463301,0.000007900156,0.8545517,0.0002165871,0.1438338,0.001218811,0.00001582329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001232329,0.0004534328,0.9972861,0.000233893,0.00002149417,0.0000141548,0.0000320713,0.00008874046,0.0006377898],"genre_scores_gemma":[0.198594,0.002393857,0.7931852,0.0004008949,0.0003067255,0.0004101475,0.0004431617,0.000444488,0.003821564],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01338858,"threshold_uncertainty_score":0.03355384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0884369715829856,"score_gpt":0.224003306797595,"score_spread":0.1355663352146094,"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."}}