{"id":"W1942789941","doi":"10.48550/arxiv.1301.0584","title":"Decayed MCMC Filtering","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Markov chain Monte Carlo; Particle filter; Mathematics; Algorithm; Convergence (economics); Applied mathematics; Markov chain; State space; Sequence (biology); Mathematical optimization; Monte Carlo method; Computer science; Statistics; Kalman filter","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002374506,0.0003064996,0.0002819005,0.0001639388,0.0001376049,0.0001672765,0.002141784,0.000301864,0.00004712767],"category_scores_gemma":[0.00002055599,0.0003585065,0.0001774228,0.0003280489,0.00006232379,0.0004489966,0.00255981,0.0006450234,0.0002799245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001273194,"about_ca_system_score_gemma":0.0001501141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001081964,"about_ca_topic_score_gemma":0.00001128314,"domain_scores_codex":[0.9982583,0.00009722989,0.0001761561,0.0008952603,0.00008677333,0.0004862615],"domain_scores_gemma":[0.9980189,0.00005685876,0.000164531,0.001344973,0.0001527648,0.0002620274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002544631,0.0001892272,0.002102321,0.0001685018,0.0001599637,0.0003445905,0.0008255373,0.1922694,0.0004844747,0.793546,0.001065411,0.008819097],"study_design_scores_gemma":[0.0002519753,0.00002588806,0.0006959325,0.0001364869,0.00005338125,0.00001083182,0.00002340523,0.9270383,0.0005475324,0.06963407,0.0008340694,0.0007481931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07010971,0.000106611,0.9229875,0.00008034967,0.0008566846,0.0001096369,0.000006284797,0.0004060869,0.005337196],"genre_scores_gemma":[0.9889693,0.00009611437,0.009511606,0.0001559875,0.0001300769,7.915305e-7,0.000007390743,0.00001672199,0.00111201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9188596,"threshold_uncertainty_score":0.9998867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1321234438859648,"score_gpt":0.1979145060466468,"score_spread":0.06579106216068195,"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."}}