{"id":"W2364341470","doi":"10.1002/cjs.11343","title":"Likelihood inflating sampling algorithm","year":2017,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Sampling (signal processing); Algorithm; Computer science; Bayesian probability; Posterior probability; Gibbs sampling; Likelihood function; Importance sampling; Set (abstract data type); Monte Carlo method; Statistics; Mathematics; Artificial intelligence; Estimation theory","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001909181,0.0003418084,0.0008291557,0.0003884663,0.000352068,0.0004134237,0.0007910867,0.0003630547,0.00006211812],"category_scores_gemma":[0.006217546,0.0003395837,0.0002121787,0.0000409029,0.0001316409,0.00007362793,0.0001401949,0.001560708,2.281206e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003805475,"about_ca_system_score_gemma":0.004698711,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003096701,"about_ca_topic_score_gemma":0.01910876,"domain_scores_codex":[0.9977031,0.0001468577,0.001032294,0.0002236665,0.0003480361,0.0005460577],"domain_scores_gemma":[0.9943519,0.0007648684,0.00207105,0.0006551102,0.001059609,0.001097454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000108351,0.00002877208,0.001043636,0.001196474,0.0005904174,0.002426672,0.003420867,0.0001544325,0.00002491197,0.02743671,0.0509834,0.9126829],"study_design_scores_gemma":[0.001058632,0.0002203873,0.0004616013,0.003873391,0.0008893795,0.0005647626,0.0008596821,0.00837081,0.00008657784,0.9086355,0.07365537,0.001323929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002651107,0.0008749549,0.9868358,0.0001751727,0.00363564,0.0002017338,0.002036509,0.00001053533,0.003578494],"genre_scores_gemma":[0.008105971,0.0001260036,0.9898204,0.00007293073,0.001378283,0.000003228116,0.00002840874,0.00008252923,0.0003822243],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.911359,"threshold_uncertainty_score":0.9999056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1326593454697426,"score_gpt":0.3720022831719525,"score_spread":0.2393429377022099,"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."}}