{"id":"W2529146392","doi":"10.22331/q-2017-04-25-5","title":"QInfer: Statistical inference software for quantum applications","year":2017,"lang":"en","type":"article","venue":"Quantum","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Army Research Office; Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Office of Naval Research; Industry Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Computer science; Benchmarking; Statistical inference; Robustness (evolution); Software; Inference; Statistical hypothesis testing; Data mining; Theoretical computer science; Data science; Computer engineering; Machine learning; Artificial intelligence; Mathematics; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01013633,0.002026618,0.001949331,0.003168293,0.001227299,0.00277418,0.005559323,0.002332208,0.04570627],"category_scores_gemma":[0.03939543,0.001942764,0.002632496,0.00244717,0.001571046,0.004202621,0.002998234,0.005231752,0.0139882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001688566,"about_ca_system_score_gemma":0.004713638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003977495,"about_ca_topic_score_gemma":0.005718021,"domain_scores_codex":[0.9959157,0.001409246,0.0004017132,0.0005239492,0.001532082,0.0002172528],"domain_scores_gemma":[0.9791626,0.01517604,0.00102585,0.002135374,0.002181574,0.0003185121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007272848,0.0003058825,0.004061127,0.003005663,0.0008422875,0.0005803739,0.0005625386,0.1178201,0.007427953,0.1745824,0.3693741,0.3207103],"study_design_scores_gemma":[0.0003152072,0.00007908898,0.001296875,0.0002669825,0.0001217931,0.0003527469,0.00004925366,0.6556789,0.01734425,0.2192708,0.1050021,0.0002220188],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0006268827,0.0001624437,0.8594841,0.0001821479,0.00007460571,0.00008706027,0.002498258,0.1353037,0.001580757],"genre_scores_gemma":[0.05263933,0.0007077552,0.8736091,0.001099543,0.0002691693,0.001454474,0.009986951,0.05538465,0.004849184],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.04570627,"threshold_uncertainty_score":0.1529027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03293827725938926,"score_gpt":0.3270139279445998,"score_spread":0.2940756506852105,"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."}}