{"id":"W6891829986","doi":"10.48550/arxiv.2106.10660","title":"Bayesian inference for continuous-time hidden Markov models with an unknown number of states","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inference; Hidden Markov model; Reversible-jump Markov chain Monte Carlo; Markov chain Monte Carlo; Bayesian inference; Hidden semi-Markov model; Bayesian probability; Context (archaeology); Markov process","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.007527155,0.001059962,0.001781439,0.002326992,0.001357425,0.002329319,0.003834576,0.002455877,0.002843033],"category_scores_gemma":[0.03157099,0.001363352,0.001886296,0.002397788,0.002977178,0.003401961,0.002587551,0.003820257,0.0005889792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003277675,"about_ca_system_score_gemma":0.002995308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02718535,"about_ca_topic_score_gemma":0.0306409,"domain_scores_codex":[0.9971831,0.001498068,0.0001115281,0.0006377103,0.0004011718,0.0001683489],"domain_scores_gemma":[0.9812937,0.0163838,0.0008691018,0.0007192633,0.0005018204,0.0002322512],"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":[0.00007688115,0.00007036399,0.002191521,0.0001217933,0.0001364704,0.0001283914,0.0001630949,0.7986909,0.0006925298,0.1659257,0.0009386336,0.03086381],"study_design_scores_gemma":[0.00000977973,0.000007247917,0.0002499568,0.00001411651,0.00001236323,0.00001639955,0.00001495727,0.9289075,0.0001882456,0.07010641,0.0004598066,0.00001317199],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005447433,0.0001591724,0.993537,0.0002244537,0.00001410232,0.00002291422,0.0000715746,0.0001216066,0.000401821],"genre_scores_gemma":[0.322154,0.0009384039,0.6713251,0.0002862489,0.0001539587,0.0003522675,0.0008523738,0.0001720571,0.003765481],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02718535,"threshold_uncertainty_score":0.0540542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04133180057113238,"score_gpt":0.2211670127628488,"score_spread":0.1798352121917164,"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."}}