{"id":"W2680474992","doi":"10.1109/icassp.2017.7952876","title":"Sequential MCMC with invertible particle flow","year":2017,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Particle filter; Markov chain Monte Carlo; Degeneracy (biology); Auxiliary particle filter; Kernel (algebra); Invertible matrix; Algorithm; Computer science; Dimension (graph theory); Applied mathematics; Inference; Gaussian; Monte Carlo method; Mathematical optimization; Mathematics; Artificial intelligence; Kalman filter; Discrete mathematics; Statistics; Extended Kalman filter; Ensemble Kalman filter","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001891029,0.0007302061,0.0009508664,0.0007883053,0.0005853793,0.0007399974,0.001465776,0.001073126,0.003050689],"category_scores_gemma":[0.008417374,0.0006240653,0.0009651023,0.001018323,0.001029329,0.001109277,0.001223068,0.001738778,0.000606841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007578889,"about_ca_system_score_gemma":0.002106948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01345106,"about_ca_topic_score_gemma":0.01143582,"domain_scores_codex":[0.9988551,0.0003478678,0.00005731928,0.0002558888,0.0003843055,0.00009964183],"domain_scores_gemma":[0.9967895,0.002131373,0.0002108662,0.0003855974,0.0004020202,0.00008063828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001579066,0.00007820998,0.001490381,0.0001127913,0.00008011402,0.0001368057,0.0001121146,0.8449117,0.002689379,0.05235238,0.001422617,0.09645562],"study_design_scores_gemma":[0.00001056887,0.0000091646,0.00007263543,0.000002418508,0.000003924589,0.00001078675,0.00000226028,0.9929706,0.0005110633,0.005882908,0.0005190878,0.000004527258],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004249755,0.0000602324,0.9946778,0.00005360891,0.00002432702,0.00003933345,0.00003545645,0.0003014511,0.0005581044],"genre_scores_gemma":[0.3563322,0.0002285172,0.6387658,0.0001447321,0.0001068359,0.0003741512,0.0004181179,0.000172739,0.003456846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01345106,"threshold_uncertainty_score":0.02674556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03046223097479669,"score_gpt":0.2556271337490255,"score_spread":0.2251649027742288,"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."}}