{"id":"W3186789731","doi":"10.3390/e23080961","title":"ABCDP: Approximate Bayesian Computation with Differential Privacy","year":2021,"lang":"en","type":"article","venue":"MDPI (MDPI AG)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Differential privacy; Approximate Bayesian computation; Computer science; Computation; Bayesian probability; Sample (material); Posterior probability; Noise (video); Secure multi-party computation; Data mining; Algorithm; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002881221,0.0002704148,0.0004183899,0.0000735707,0.000150207,0.0001253656,0.0001766569,0.0001175367,0.0001653739],"category_scores_gemma":[0.0001972244,0.0002182659,0.0001290617,0.0002243707,0.00005935108,0.0001043495,0.0001381403,0.000224236,8.01165e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000059598,"about_ca_system_score_gemma":0.00008590677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001340965,"about_ca_topic_score_gemma":0.00005015425,"domain_scores_codex":[0.9982213,0.0002367318,0.0003531656,0.0004383026,0.0003719711,0.0003785697],"domain_scores_gemma":[0.998738,0.0002631363,0.0001649314,0.000496063,0.000188338,0.0001495359],"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":[0.001336576,0.005772549,0.008387441,0.00671808,0.002205901,0.002739705,0.03045868,0.0004546064,0.06501525,0.4436756,0.06340132,0.3698342],"study_design_scores_gemma":[0.03095441,0.002341999,0.008162388,0.003079668,0.002716927,0.001563077,0.008176517,0.309735,0.1956678,0.331395,0.09705488,0.009152408],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.290444,0.00007349251,0.6964037,0.0004360414,0.0003284551,0.0003236568,0.00001625879,0.0001968911,0.01177744],"genre_scores_gemma":[0.7563753,0.00001688149,0.2388235,0.0001717007,0.0003004655,0.0000469568,0.00007920132,0.00008131822,0.004104616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4659314,"threshold_uncertainty_score":0.8900626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04492684427359561,"score_gpt":0.3267668623492428,"score_spread":0.2818400180756472,"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."}}