{"id":"W1982891217","doi":"10.1111/1467-9868.00219","title":"Bayesian Inference in Hidden Markov Models Through the Reversible Jump Markov Chain Monte Carlo Method","year":2000,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":205,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Royal Swedish Academy of Sciences; University of Glasgow; Trent University; Core Research for Evolutional Science and Technology; Nottingham Trent University","keywords":"Markov chain Monte Carlo; Reversible-jump Markov chain Monte Carlo; Markov chain; Variable-order Bayesian network; Hidden Markov model; Inference; Markov model; Variable-order Markov model; Bayesian inference; Statistical physics; Computer science; Bayesian probability; Hidden semi-Markov model; Monte Carlo method; Jump; Mathematics; Algorithm; Artificial intelligence; Statistics; Machine learning; Physics","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":[0.00920012,0.0009374409,0.001927249,0.002298337,0.000925776,0.001945773,0.002956089,0.001775784,0.00374507],"category_scores_gemma":[0.03268361,0.001236441,0.001894891,0.001842648,0.002170108,0.003121662,0.001801716,0.003592191,0.0006884568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002184984,"about_ca_system_score_gemma":0.002510791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01036277,"about_ca_topic_score_gemma":0.01062431,"domain_scores_codex":[0.9956028,0.002991228,0.0001524206,0.0004969836,0.0005608429,0.0001957242],"domain_scores_gemma":[0.9673767,0.02962724,0.001025357,0.001068607,0.0006462885,0.0002558997],"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":[0.00008056056,0.00005891767,0.001248359,0.0001199857,0.0001890591,0.0001448972,0.0001316925,0.6720582,0.0004678395,0.2919076,0.001096548,0.0324963],"study_design_scores_gemma":[0.00001767245,0.000007050837,0.0001255376,0.00001750596,0.00001532718,0.00001728417,0.000006431785,0.8538486,0.000155006,0.145351,0.0004221149,0.00001637234],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004698701,0.0002343337,0.9939021,0.0002267007,0.00001746673,0.00002929821,0.00009762484,0.0002374423,0.0005564695],"genre_scores_gemma":[0.3568414,0.001114161,0.6375389,0.000223513,0.0001996224,0.0004463428,0.0006658491,0.0002400147,0.002730234],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01036277,"threshold_uncertainty_score":0.04865551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04077475466133923,"score_gpt":0.3213892502813812,"score_spread":0.280614495620042,"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."}}