{"id":"W4400905217","doi":"10.1109/lra.2024.3432350","title":"A Hessian for Gaussian Mixture Likelihoods in Nonlinear Least Squares","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hessian matrix; Least-squares function approximation; Nonlinear system; Non-linear least squares; Mathematics; Applied mathematics; Gaussian; Mixture model; Mathematical optimization; Statistics; Explained sum of squares; Chemistry; Physics; Computational chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003022155,0.0001519622,0.0001670868,0.0002041123,0.00007641945,0.0004140144,0.0002136475,0.00008479924,0.000001386783],"category_scores_gemma":[0.00001158784,0.0001303763,0.00006653259,0.0002950221,0.00002952664,0.0003750086,0.00002952057,0.0001464404,0.000004963746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002842798,"about_ca_system_score_gemma":0.00003988708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007127027,"about_ca_topic_score_gemma":0.000008920324,"domain_scores_codex":[0.9989645,0.00005734578,0.0002347895,0.0003650192,0.0001332212,0.0002451148],"domain_scores_gemma":[0.9995615,0.00009385729,0.00004048459,0.000210518,0.0000189246,0.00007473833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001129798,0.0001104704,0.000130053,0.0008672164,0.00006850065,0.0001258015,0.004257823,0.01107711,0.0193393,0.353624,0.01393449,0.5964539],"study_design_scores_gemma":[0.0002270627,0.00003445741,0.0003489936,0.0001862012,0.000009719141,0.0000195178,0.000006194204,0.9849164,0.0005465859,0.01027204,0.00323297,0.0001998775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002058443,0.0003545244,0.9679385,0.02839619,0.0007590862,0.0002214393,0.000007040889,0.0001844034,0.00008039697],"genre_scores_gemma":[0.1367274,0.00002496694,0.860722,0.002182536,0.0002386506,0.00002540507,0.000006503782,0.00001797184,0.0000545841],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9738393,"threshold_uncertainty_score":0.5316592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01265673173909768,"score_gpt":0.2723217609700466,"score_spread":0.2596650292309489,"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."}}