{"id":"W2006677025","doi":"10.1080/03610926.2011.636168","title":"Markov-Correlated Poisson Processes","year":2013,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Markov chain; Generalization; Markov renewal process; Bernoulli trial; Computer science; Poisson distribution; Flexibility (engineering); Markov process; Variable-order Markov model; Markov model; Term (time); Simple (philosophy); Markov chain Monte Carlo; Markov property; Bernoulli's principle; Applied mathematics; Mathematics; Statistical physics; Econometrics; Statistics; Artificial intelligence; Machine learning; Bayesian probability; Engineering; 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":[0.004558807,0.0008874548,0.001395626,0.001138688,0.0007037789,0.001903045,0.002498271,0.002048848,0.006594137],"category_scores_gemma":[0.01716641,0.000668936,0.001325706,0.001575917,0.0023049,0.003436025,0.001615384,0.002876277,0.001455125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001407049,"about_ca_system_score_gemma":0.001674826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00260352,"about_ca_topic_score_gemma":0.002070426,"domain_scores_codex":[0.9968246,0.001351379,0.0001715182,0.0006056717,0.0007620628,0.0002847939],"domain_scores_gemma":[0.9905165,0.006147711,0.001126436,0.0009826801,0.0009331962,0.0002934039],"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":[0.00002835595,0.00002436904,0.000718335,0.00004474994,0.00002644881,0.0002673682,0.00008599723,0.04435973,0.0004352354,0.9478751,0.001371042,0.00476318],"study_design_scores_gemma":[0.00004955638,0.00004273738,0.0005380743,0.00004490455,0.00004412699,0.0003734639,0.00003429715,0.4329821,0.0004799237,0.5603266,0.005038524,0.00004567594],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01708831,0.0006192995,0.9691852,0.001103176,0.0003088717,0.0001425931,0.0003288009,0.0001729264,0.01105086],"genre_scores_gemma":[0.7551401,0.003827367,0.1953769,0.001700893,0.001350837,0.001153718,0.0009399666,0.0001891006,0.04032099],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006594137,"threshold_uncertainty_score":0.02410954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07478520099467104,"score_gpt":0.4558085368751254,"score_spread":0.3810233358804543,"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."}}