{"id":"W3006840371","doi":"10.1109/ssci44817.2019.9003076","title":"Efficient Computation of Log-likelihood Function in Clustering Overdispersed Count Data Using Multinomial Beta-Liouville Distribution","year":2019,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Multinomial distribution; Cluster analysis; Count data; Dirichlet distribution; Mathematics; Computer science; Algorithm; Statistics; Poisson distribution","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":[0.004022606,0.0007978779,0.001645163,0.003310457,0.001170457,0.001936555,0.003204635,0.001908617,0.002010244],"category_scores_gemma":[0.01560311,0.0007375393,0.001453976,0.002713119,0.001195846,0.003215654,0.002524553,0.001900765,0.001171121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002011761,"about_ca_system_score_gemma":0.002037617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008498866,"about_ca_topic_score_gemma":0.008895567,"domain_scores_codex":[0.9978603,0.0007895433,0.0001340716,0.0004717699,0.000597993,0.0001463562],"domain_scores_gemma":[0.9948617,0.003526078,0.0003190617,0.0004251692,0.0006866292,0.0001813585],"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":[0.0002092399,0.0001079139,0.006639897,0.0002259674,0.0001311538,0.0002270777,0.0004566793,0.5922995,0.007412778,0.04161624,0.002592588,0.348081],"study_design_scores_gemma":[0.000006665008,0.00001261089,0.0003761739,0.000007434596,0.000004773349,0.00005775292,0.00003114087,0.9843312,0.0008454583,0.01376249,0.0005491801,0.00001503911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005196409,0.00008803525,0.9940271,0.00005966286,0.000008891838,0.00001946703,0.00002875609,0.0003974887,0.0001741694],"genre_scores_gemma":[0.1240139,0.0002026404,0.873187,0.0001324902,0.00004295032,0.0002388446,0.0005282076,0.0002434912,0.001410563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008498866,"threshold_uncertainty_score":0.02127385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03387391639320726,"score_gpt":0.2900931648999279,"score_spread":0.2562192485067206,"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."}}