{"id":"W2095999217","doi":"","title":"Variational bounds for mixed-data factor analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Binary data; Categorical variable; Exponential family; Algorithm; Quadratic equation; Simple (philosophy); Binary number; Missing data; Upper and lower bounds; Exponential function; Mathematics; Applied mathematics; Factor (programming language); Computer science; Mathematical optimization; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003869143,0.000113294,0.0002704502,0.0003541739,0.0001545656,0.0004980324,0.001739898,0.00009237334,0.01115843],"category_scores_gemma":[0.005429323,0.00007795621,0.00018627,0.001245064,0.00007574261,0.0006247672,0.0003225453,0.0001042228,0.0002843713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001344445,"about_ca_system_score_gemma":0.0000860927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004540874,"about_ca_topic_score_gemma":0.0001750413,"domain_scores_codex":[0.9975029,0.000130961,0.0004675569,0.0006629793,0.001029863,0.000205694],"domain_scores_gemma":[0.9945728,0.003382404,0.0001394986,0.001494963,0.0002764905,0.0001338503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002005373,0.0005774639,0.07185369,0.000003789526,0.001255411,0.00000299147,0.0005441313,0.000323999,0.3606494,0.2679904,0.1315579,0.1650403],"study_design_scores_gemma":[0.0007450326,0.0001285329,0.1591155,6.678505e-7,0.000244523,0.00000392832,0.0003595354,0.5251324,0.02479785,0.07655941,0.2123967,0.0005159956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03246647,0.00001226707,0.955208,0.0004972268,0.0009755783,0.000184112,0.0003831069,0.0000436965,0.01022957],"genre_scores_gemma":[0.3828995,2.492294e-7,0.6109343,0.0001710103,0.0001131093,0.00001332008,0.00006128782,0.000006314308,0.005800977],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5248083,"threshold_uncertainty_score":0.9897455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3099378766018399,"score_gpt":0.5271086331942241,"score_spread":0.2171707565923842,"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."}}