{"id":"W2161202741","doi":"10.5555/1036843.1036873","title":"From fields to trees","year":2004,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gibbs sampling; Markov chain Monte Carlo; Graphical model; Tree (set theory); Sampling (signal processing); Computer science; Focus (optics); Markov chain; Importance sampling; Posterior probability; Algorithm; Mathematics; Belief propagation; Statistics; Artificial intelligence; Bayesian probability; Monte Carlo method; Machine learning; Combinatorics","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":[0.002693632,0.0007473407,0.001036635,0.002170071,0.001076214,0.002397758,0.001693661,0.001870382,0.007348331],"category_scores_gemma":[0.01956465,0.0009683582,0.001390755,0.002193641,0.00269904,0.005321384,0.002590512,0.002929278,0.001258716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001865375,"about_ca_system_score_gemma":0.001572107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004923123,"about_ca_topic_score_gemma":0.004238567,"domain_scores_codex":[0.9981632,0.0008307121,0.00008022545,0.0003352838,0.0004549392,0.0001358049],"domain_scores_gemma":[0.9922454,0.005948545,0.0004185723,0.0007719699,0.0004263971,0.0001890645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003769356,0.00002478751,0.0004909578,0.0001062798,0.00002931686,0.00004037778,0.0001769958,0.1683224,0.0004923986,0.7614397,0.003711224,0.06512783],"study_design_scores_gemma":[0.00001558681,0.00001071666,0.00009722353,0.00003249622,0.000007287635,0.00002691294,0.00001268314,0.2511586,0.0002536615,0.7420385,0.006334193,0.00001210202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002222463,0.0003716046,0.9952621,0.0002668574,0.0000326703,0.00002289415,0.00007841922,0.0001790145,0.001563916],"genre_scores_gemma":[0.1355969,0.001893766,0.8548822,0.0007715953,0.0002940032,0.0003550288,0.0005620466,0.0004264167,0.0052181],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007348331,"threshold_uncertainty_score":0.02458262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04739542792282484,"score_gpt":0.3219355585509258,"score_spread":0.274540130628101,"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."}}