Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

May 15, 1996, Vol. 8, No. 4, Pages 843-854
(doi: 10.1162/neco.1996.8.4.843)
© 1996 Massachusetts Institute of Technology
Using Neural Networks to Model Conditional Multivariate Densities
Article PDF (1.08 MB)
Abstract

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.