{"id":"W2103225616","doi":"10.1109/tip.2006.877522","title":"Fusion of Hidden Markov Random Field Models and Its Bayesian Estimation","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Markov random field; Bayesian probability; Computer science; Artificial intelligence; Markov process; Hidden Markov model; Random field; Markov model; Pattern recognition (psychology); Field (mathematics); Estimation; Markov chain; Mathematics; Machine learning; Statistics; Image segmentation; Image (mathematics); Engineering","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":[0.007603214,0.00105345,0.002167933,0.001732434,0.000523245,0.001541924,0.002152174,0.001961478,0.001260914],"category_scores_gemma":[0.01563242,0.001048046,0.001569151,0.001588056,0.001559424,0.003609326,0.001983968,0.002362815,0.0005997085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001485759,"about_ca_system_score_gemma":0.001245113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005099203,"about_ca_topic_score_gemma":0.003121366,"domain_scores_codex":[0.9970495,0.001337522,0.0001533836,0.0005898913,0.0006679616,0.0002017782],"domain_scores_gemma":[0.9941711,0.004258187,0.0004815804,0.0004516541,0.0005420253,0.00009554705],"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":[0.0001707294,0.00005938489,0.0008940112,0.0001612501,0.0001534307,0.0001104536,0.0001661361,0.7986114,0.001701607,0.1243673,0.0008145683,0.07278971],"study_design_scores_gemma":[0.000005375043,0.0000108783,0.0001002126,0.00001008622,0.00001150434,0.00001590845,0.000004071354,0.9711553,0.0003238302,0.02804052,0.0003076917,0.00001448086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003310642,0.000285977,0.9957624,0.0001237038,0.00001866857,0.00001130006,0.00003167114,0.0001188651,0.000336782],"genre_scores_gemma":[0.5431287,0.001659584,0.4500578,0.0003143387,0.0002604521,0.0002503977,0.0005684369,0.0001601823,0.003600091],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007603214,"threshold_uncertainty_score":0.04021013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01241892220575321,"score_gpt":0.262910813487139,"score_spread":0.2504918912813858,"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."}}