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Bayesian statistics / Design of experiments / Statistical methods / Optimal design / Mathematical model / Kullback–Leibler divergence / Bayesian inference / Maximum likelihood / Dynamical system / Statistics / Statistical theory / Systems engineering


Optimized Expected Information Gain for Nonlinear Dynamical Systems Alberto Giovanni Busetto1,2,3 [removed] Cheng Soon Ong1
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Document Date: 2013-12-12 02:50:01


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City

Zurich / Beijing / Montreal / /

Company

Princeton University Press / Cambridge University Press / Neural Networks / Nonlinear Dynamical Systems / /

Country

Switzerland / Canada / China / /

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IndustryTerm

distinct chemical entity / stochastic and deterministic dynamical systems / uncertain systems / chemical reactions / biochemical systems / iterative online design / reaction network / biological networks / static systems / distinct chemical reactions / biological systems / continuous-time continuous-valued systems / finance / chemical species / exhaustive search / linear systems / dynamical systems / /

Organization

Cambridge University / African Union / Department of Computer Science / Princeton University / Swiss National Science Foundation / Competence Center for Systems Physiology and Metabolic Diseases / /

Person

Cheng Soon / Ronald A. Fisher / Uwe Sauer / Stelling Sauer / Alberto Giovanni / /

Position

author / King / model / given all the available knowledge / /

PublishedMedium

Machine Learning / Nature Biotechnology / /

Technology

Biotechnology / fluid dynamics / cell signaling / Machine Learning / /

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