{"id":"W2137440488","doi":"","title":"Fast maximum a posteriori inference in Monte Carlo state spaces","year":2005,"lang":"en","type":"article","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Viterbi algorithm; Inference; Computer science; Maximum a posteriori estimation; Computation; Monte Carlo method; Approximate inference; Algorithm; A priori and a posteriori; Range (aeronautics); State (computer science); Artificial intelligence; Hidden Markov model; Mathematics; Maximum likelihood; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009060738,0.0002674468,0.0004245341,0.001496799,0.000675877,0.0001225888,0.003071403,0.0001007508,0.00008455153],"category_scores_gemma":[0.0001035722,0.000336673,0.0001773738,0.001648407,0.0006225019,0.001605481,0.002625359,0.001118137,0.00003180104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002942737,"about_ca_system_score_gemma":0.0004290005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004367038,"about_ca_topic_score_gemma":0.007565083,"domain_scores_codex":[0.9964601,0.000669078,0.0001832891,0.0007894334,0.0008908085,0.001007308],"domain_scores_gemma":[0.9978251,0.000488067,0.0001687368,0.0007950064,0.0003093065,0.0004138181],"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":[0.001652044,0.001187319,0.1277326,0.000315572,0.0002885122,0.002950749,0.05825489,0.02699188,0.001863742,0.02815297,0.002689091,0.7479206],"study_design_scores_gemma":[0.004607036,0.001439078,0.09311213,0.0003215006,0.00002298295,0.00003017707,0.01421671,0.4515631,0.0001046537,0.004178097,0.4293466,0.001057892],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7910053,0.000069148,0.17336,0.005676042,0.0001028491,0.0005414663,0.000137615,0.0002082079,0.02889943],"genre_scores_gemma":[0.93242,0.0008200502,0.05806561,0.00003575498,0.00003984673,2.072952e-7,0.00001392304,0.00001695156,0.008587613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7468627,"threshold_uncertainty_score":0.9999085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02019099077922133,"score_gpt":0.2612807071089224,"score_spread":0.2410897163297011,"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."}}