{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004794206,0.001159433,0.001800531,0.001556886,0.001150893,0.002280032,0.00390895,0.00324416,0.005267526],"category_scores_gemma":[0.03339928,0.001075262,0.001526726,0.002088567,0.00217837,0.002707726,0.002155383,0.004163648,0.001453969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002825906,"about_ca_system_score_gemma":0.003463935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01791266,"about_ca_topic_score_gemma":0.01646723,"domain_scores_codex":[0.996425,0.001255832,0.0002064286,0.0007471069,0.001098252,0.0002673237],"domain_scores_gemma":[0.9858041,0.008736888,0.0006700619,0.002605545,0.001950404,0.0002329363],"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":[0.0002882886,0.00008359391,0.002069963,0.0001723183,0.0001579369,0.0001518127,0.0001653063,0.7285013,0.003229728,0.1540207,0.005093684,0.1060655],"study_design_scores_gemma":[0.00001550206,0.000008587898,0.00009705936,0.00001079321,0.000008265433,0.00002118172,0.000004861672,0.9770427,0.0007795775,0.02071744,0.00128313,0.00001081455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002623042,0.00009429332,0.9958383,0.0001145831,0.00003399296,0.00003622391,0.00009259827,0.0004574056,0.0007095509],"genre_scores_gemma":[0.1900047,0.0003255068,0.8031759,0.0004153326,0.00009757254,0.0003694404,0.001059798,0.0003430675,0.004208756],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01791266,"threshold_uncertainty_score":0.03561682,"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."}}