{"id":"W1995050491","doi":"10.1016/j.csda.2007.09.009","title":"Interacting sequential Monte Carlo samplers for trans-dimensional simulation","year":2007,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of British Columbia","funders":"","keywords":"Markov chain Monte Carlo; Particle filter; Monte Carlo method; Importance sampling; Sampling (signal processing); Population; Resampling; Algorithm; Inference; Computer science; Convergence (economics); Slice sampling; Bayesian inference; Mathematical optimization; Statistical physics; State space; Hybrid Monte Carlo; Mathematics; Bayesian probability; Statistics; Artificial intelligence; Physics; Kalman filter; Filter (signal processing)","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":[],"consensus_categories":[],"category_scores_codex":[0.001417638,0.0001804237,0.0003017486,0.0003540729,0.0002694059,0.0002060354,0.0007426753,0.00006010384,0.0000298565],"category_scores_gemma":[0.0003067147,0.0001864793,0.0001321558,0.000795058,0.00004259107,0.0005768475,0.0002151785,0.0001261556,0.000003695054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006828495,"about_ca_system_score_gemma":0.000093813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002334226,"about_ca_topic_score_gemma":0.0003123101,"domain_scores_codex":[0.9978426,0.0001038217,0.0005607978,0.0006868942,0.0004968834,0.000309068],"domain_scores_gemma":[0.9957241,0.002975226,0.000236485,0.0005063374,0.0004162649,0.0001415662],"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":[0.0000280242,0.00004223674,0.0001608656,0.0000123822,0.0005595876,0.000009866629,0.0002452553,0.8828508,0.00002478312,0.03600378,0.0008967599,0.07916564],"study_design_scores_gemma":[0.0002893369,0.00002293636,0.002940178,0.000006262761,0.000470566,0.000002638852,0.000004757107,0.9573147,0.000007131375,0.03737098,0.001370987,0.0001995332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007532408,0.00004390267,0.996292,0.0001539171,0.0002197101,0.0001771719,0.002277352,0.00005780957,0.00002488132],"genre_scores_gemma":[0.3403266,0.00000116381,0.657285,0.0001669086,0.00008200065,0.000002734296,0.0021024,0.000008687476,0.00002456187],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3395734,"threshold_uncertainty_score":0.7604407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09360254122331375,"score_gpt":0.4022379766698072,"score_spread":0.3086354354464935,"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."}}