Computational optimization of associative learning experiments

With computational biology striving to provide more accurate theoretical accounts of biological systems, use of increasingly complex computational models seems inevitable. However, this trend engenders a challenge of optimal experimental design: due to the flexibility of complex models, it is diffic...

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Bibliographic Details
Published in:	PLoS computational biology Vol. 16; no. 1; p. e1007593
Main Authors:	Melinscak, Filip, Bach, Dominik R.
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 01.01.2020 Public Library of Science (PLoS)
Subjects:	Algorithms Associative learning Bayes Theorem Bayesian analysis Biology Biology and Life Sciences Computational Biology - methods Computer applications Computer Simulation Datasets Design of experiments Design optimization Diagnostic systems Expected utility Experimental design Experiments Humans Learning theory Mathematical models Medical imaging Methods Models, Psychological Neurosciences Optimization Parameter estimation Physical Sciences Psychiatry Psychotherapy Research and Analysis Methods Research Design Simulation Social Sciences Software Utility functions Switzerland
ISSN:	1553-7358, 1553-734X, 1553-7358
Online Access:	Get full text
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