Inversion of hierarchical Bayesian models using Gaussian processes

NeuroImage

Volumn 118, Issue , 2015, Pages 133-145

  Lomakina, Ekaterina I ^a,b   Paliwal, Saee ^b   Diaconescu, Andreea O ^b   Brodersen, Kay H ^a,b   Aponte, Eduardo A ^b   Buhmann, Joachim M ^a   Stephan, Klaas E ^b,c  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF ZURICH   (Switzerland)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Bayesian inference; Dynamic causal modelling; Gaussian processes; Global optimisation; Hierarchical models; MCMC

Indexed keywords

ARTICLE; BAYES THEOREM; DIGITAL FILTERING; DYNAMIC CAUSAL MODELING; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GAUSSIAN PROCESS OPTIMIZATION; HIERARCHICAL BAYESIAN MODEL; HIERARCHICAL GAUSSIAN FILTER; MARKOV CHAIN MONTE CARLO METHOD; MATHEMATICAL COMPUTING; MONTE CARLO METHOD; NEUROIMAGING; PRIORITY JOURNAL; ALGORITHM; BIOLOGICAL MODEL; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; HUMAN; NORMAL DISTRIBUTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES;

ALGORITHMS; BAYES THEOREM; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; HUMANS; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; NORMAL DISTRIBUTION;

EID: 84937566220     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2015.05.084     Document Type: Article

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