Using Neural Networks to Model Conditional Multivariate Densities
Keyword(s):
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.
2020 ◽
2020 ◽
1994 ◽
Vol 02
(02)
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pp. 193-228
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1995 ◽
Vol 09
(10)
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pp. 1159-1169
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Keyword(s):
2019 ◽
Vol 2019
(1)
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pp. 153-158
2020 ◽
Vol 64
(3)
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pp. 30502-1-30502-15
2019 ◽
Vol 3
(3)
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