Non-homogeneous dynamic Bayesian networks for continuous data

Machine Learning

Volumn 83, Issue 3, 2011, Pages 355-419

  Grzegorczyk, Marco ^a   Husmeier, Dirk ^b  

^a TU DORTMUND UNIVERSITY   (Germany)

^b BIOMATHEMATICS AND STATISTICS SCOTLAND   (United Kingdom)

Author keywords

Arabidopsis; Circadian regulation; Dynamic Bayesian networks; Dynamic programming; Gene regulatory network; Multiple changepoint process; Non homogeneity; Precision recall (PR) curve; Receiver operator characteristics (ROC) curve; Reversible jump Markov chain Monte Carlo (RJMCMC)

Indexed keywords

ARABIDOPSIS; CHANGE-POINTS; CIRCADIAN REGULATION; DYNAMIC BAYESIAN NETWORKS; GENE REGULATORY NETWORK; NONHOMOGENEITY; PRECISION-RECALL (PR) CURVE; RECEIVER OPERATOR CHARACTERISTICS (ROC) CURVE; REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO;

COMPUTATIONAL COMPLEXITY; DISTRIBUTED PARAMETER NETWORKS; DYNAMIC PROGRAMMING; GENES; INFERENCE ENGINES; INTELLIGENT NETWORKS; MARKOV PROCESSES; PROBABILITY DISTRIBUTIONS;

BAYESIAN NETWORKS;

EID: 79958861169     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-010-5230-7     Document Type: Article

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