Optimized expected information gain for nonlinear dynamical systems

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 97-104

  Busetto, Alberto Giovanni ^a,b,c   Ong, Cheng Soon ^a   Buhmann, Joachim M ^a,b  

^a ETH ZURICH   (Switzerland)

^b Competence Center for Systems Physiology and Metabolic Diseases ^*  (Switzerland)

^c Life Science Zurich   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE MODELS; BAYESIAN MODEL; BIOCHEMICAL MODEL; D-OPTIMAL; D-OPTIMAL DESIGNS; GAUSSIAN NOISE; INFORMATION GAIN; KULLBACK LEIBLER DIVERGENCE; LEARNING APPROACH; MODEL IDENTIFICATION; MODEL SELECTION CRITERIA; MODEL SELECTION PROBLEM; OPTIMAL SEQUENTIAL; POSTERIOR PROBABILITY; SIGNALING PATHWAYS; SYSTEMS BIOLOGY; TIME-DEPENDENT VARIABLES;

BAYESIAN NETWORKS; BIOLOGY; GAUSSIAN NOISE (ELECTRONIC); NONLINEAR DYNAMICAL SYSTEMS; OPTIMIZATION; ROBOT LEARNING;

DYNAMICAL SYSTEMS;

EID: 71149121686     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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