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Neural Computation, Vol 8, 843-854, Copyright © 1996 by The MIT Press

ARTICLES

Using neural networks to model conditional multivariate densities

PM Williams
School of Cognitive and Computing Sciences, University of Sussex, Falmer, Brighton, England.

Neural network outputs are interpreted as parameters of statistical distributions. This allows us to fit conditional distributions in which the parameters depend on the inputs to the network. We exploit this in modeling multivariate data, including the univariate case, in which there may be input-dependent (e.g., time-dependent) correlations between output components. This provides a novel way of modeling conditional correlation that extends existing techniques for determining input-dependent (local) error bars.




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J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1996 by The MIT Press.