{"id":"W2145677862","doi":"10.1111/1467-9868.00289","title":"Maximum Likelihood Estimation for Spatial Models by Markov Chain Monte Carlo Stochastic Approximation","year":2001,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Division of Mathematical Sciences","keywords":"Markov chain Monte Carlo; Metropolis–Hastings algorithm; Monte Carlo method; Gibbs sampling; Algorithm; Markov chain; Computer science; Stochastic approximation; Forward algorithm; Hybrid Monte Carlo; Monte Carlo algorithm; Mathematical optimization; Markov model; Mathematics; Variable-order Markov model; Statistics; Artificial intelligence; Machine learning; Bayesian probability","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":[0.005591975,0.0006750536,0.001481781,0.001685894,0.0005533324,0.001267167,0.002193103,0.001386982,0.002559825],"category_scores_gemma":[0.0202873,0.000894625,0.001266064,0.00149798,0.001325941,0.001955457,0.00165676,0.001822019,0.0007862801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087823,"about_ca_system_score_gemma":0.001744905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004414854,"about_ca_topic_score_gemma":0.004318621,"domain_scores_codex":[0.9972252,0.001740574,0.0001049872,0.0003314454,0.0004794158,0.0001184585],"domain_scores_gemma":[0.9879578,0.01032076,0.0004765204,0.0005616324,0.0005662469,0.0001170951],"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":[0.00007379883,0.00005581721,0.0013749,0.0001226091,0.0001244649,0.00008427191,0.0001140387,0.8056881,0.001192047,0.1094358,0.001257909,0.08047622],"study_design_scores_gemma":[0.00000972365,0.00000485062,0.0001237172,0.00000708045,0.000003926785,0.00001326557,0.000004114368,0.9632789,0.0002214004,0.03590634,0.0004181412,0.000008545761],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002255414,0.00005214,0.9973617,0.00004652862,0.00000442108,0.00001180405,0.00001409291,0.00009908777,0.0001546496],"genre_scores_gemma":[0.1248161,0.0001951519,0.8731496,0.00006656026,0.00004092939,0.0002315206,0.0002271394,0.000131963,0.001141067],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005591975,"threshold_uncertainty_score":0.02957356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07012751073827549,"score_gpt":0.3516130782871342,"score_spread":0.2814855675488587,"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."}}