{"id":"W3101802995","doi":"","title":"Optimal Scaling of Random Walk Metropolis algorithms with\\nDiscontinuous target densities","year":2007,"lang":"en","type":"article","venue":"MIMS EPrints (University of Southampton)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Random walk; Curse of dimensionality; Mathematics; Metropolis–Hastings algorithm; Convergence (economics); Scaling; Markov chain; Sequence (biology); Algorithm; Rate of convergence; Markov process; Probability density function; Stochastic process; Diffusion process; Diffusion; Applied mathematics; Mathematical optimization; Statistical physics; Computer science; Markov chain Monte Carlo; Statistics; Monte Carlo method; Physics","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":[0.005089776,0.0006855152,0.001179694,0.0006683666,0.000663815,0.001820361,0.001535283,0.001292207,0.002935257],"category_scores_gemma":[0.02253309,0.000593533,0.0005182402,0.0006922039,0.001799532,0.003108531,0.00188427,0.001404972,0.0006130671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001439146,"about_ca_system_score_gemma":0.001187717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001545043,"about_ca_topic_score_gemma":0.001135882,"domain_scores_codex":[0.9984115,0.0008223135,0.0000883836,0.0002544475,0.000270543,0.0001526875],"domain_scores_gemma":[0.9923801,0.005610737,0.0004890966,0.0006705196,0.0005128917,0.0003367003],"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":[0.0002378995,0.0001444462,0.001070449,0.0001587263,0.00004271397,0.00007919112,0.0001938055,0.7265795,0.002617418,0.2197805,0.001567314,0.04752807],"study_design_scores_gemma":[0.00001491339,0.00002210015,0.00004164244,0.000006847235,0.000002978869,0.000006364322,0.000009501042,0.9702569,0.0004480348,0.02888479,0.0003005387,0.00000538934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06809768,0.00052719,0.9248439,0.0006328544,0.0000999348,0.0001334593,0.00003962404,0.0004369388,0.00518844],"genre_scores_gemma":[0.6791213,0.0005085114,0.3153837,0.0002195984,0.0001140423,0.0004525776,0.000118234,0.0002770314,0.003805012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005089776,"threshold_uncertainty_score":0.02691764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02672775531838749,"score_gpt":0.2668259879196557,"score_spread":0.2400982326012682,"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."}}