{"id":"W2168123988","doi":"10.1142/s0219025703001316","title":"THE LAW OF LARGE NUMBERS AND THE LAW OF THE ITERATED LOGARITHM FOR INFINITE DIMENSIONAL INTERACTING DIFFUSION PROCESSES","year":2003,"lang":"en","type":"article","venue":"Infinite Dimensional Analysis Quantum Probability and Related Topics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Dirichlet form; Mathematics; Law of the iterated logarithm; Iterated logarithm; Law of large numbers; Logarithm; Dirichlet process; Dirichlet distribution; Statistical physics; Markov process; Markov chain; Measure (data warehouse); Diffusion; Law; Mathematical analysis; Random variable; Physics; Computer science; Statistics","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":[0.003045031,0.0005396285,0.0009463251,0.001463516,0.001190495,0.003039056,0.001327833,0.001588972,0.002172912],"category_scores_gemma":[0.01772288,0.000399719,0.0008760736,0.0008170654,0.00558732,0.003885809,0.001789552,0.002315283,0.0003010563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001841339,"about_ca_system_score_gemma":0.0009076967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001293692,"about_ca_topic_score_gemma":0.0007232336,"domain_scores_codex":[0.9986237,0.0006244332,0.00006374798,0.0001541444,0.0004033594,0.0001306661],"domain_scores_gemma":[0.9931946,0.004772917,0.000731257,0.0004140923,0.0004174582,0.0004697475],"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":[0.000009069776,0.000007702353,0.0002458917,0.00001765024,0.000006544537,0.00006718538,0.0001065002,0.005264587,0.0002826121,0.9926612,0.0002258455,0.001105144],"study_design_scores_gemma":[0.000009925426,0.000008746068,0.0002535597,0.00001663701,0.000004530466,0.0000743218,0.00002447045,0.0882734,0.0001503349,0.910571,0.0005989199,0.00001412335],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3109506,0.004430677,0.6377,0.004601188,0.0002226216,0.00006021612,0.0001474787,0.0002462775,0.04164095],"genre_scores_gemma":[0.9586481,0.001417555,0.0333898,0.0002713182,0.0003170737,0.0001464901,0.0001004356,0.00006967092,0.005639445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003045031,"threshold_uncertainty_score":0.01610386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02639593905651278,"score_gpt":0.3012819865083622,"score_spread":0.2748860474518494,"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."}}