{"id":"W4416051414","doi":"10.48550/arxiv.2508.15665","title":"Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European and Developing Countries Clinical Trials Partnership; Medical Research Council; National Institutes of Health; Foreign, Commonwealth and Development Office; Engineering and Physical Sciences Research Council; University of Waterloo; Natural Sciences and Engineering Research Council of Canada; European Commission; Bill and Melinda Gates Foundation","keywords":"Inference; Bayes' theorem; Bayesian inference; Bayesian probability; Human immunodeficiency virus (HIV); Gaussian process; Markov chain Monte Carlo; Monte Carlo method","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007108127,0.0007178317,0.001526102,0.00159127,0.0006128489,0.00189791,0.00233813,0.00114953,0.008178424],"category_scores_gemma":[0.03565861,0.001229972,0.00125243,0.001968728,0.0009771432,0.001844226,0.002141426,0.002737308,0.001792935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001628347,"about_ca_system_score_gemma":0.003574294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01526251,"about_ca_topic_score_gemma":0.02207831,"domain_scores_codex":[0.9978601,0.001180695,0.0001180661,0.0002322281,0.0004687503,0.0001400426],"domain_scores_gemma":[0.9885247,0.009165351,0.0003803429,0.0007478841,0.001022755,0.0001588479],"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":[0.0001042866,0.00007641576,0.002786566,0.0002038142,0.0001277918,0.0001102781,0.0001422211,0.7152718,0.0008181293,0.1624589,0.007529119,0.1103706],"study_design_scores_gemma":[0.00001472695,0.000005275703,0.0001960222,0.00001822665,0.000005792044,0.00001252533,0.000009333263,0.951914,0.0002033546,0.04635717,0.001255857,0.000007689378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002345796,0.0001020283,0.9961409,0.0001125568,0.0000191813,0.00002950509,0.0001746623,0.0003609765,0.0007144418],"genre_scores_gemma":[0.1256087,0.0003928948,0.8674564,0.0002005063,0.0001187021,0.0003416117,0.001152084,0.0005001799,0.004228955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01526251,"threshold_uncertainty_score":0.03759181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09788856410248475,"score_gpt":0.3799266372202439,"score_spread":0.2820380731177591,"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."}}