A convolutional noise-to-trajectory network trained with only an observation-matching loss is claimed to estimate conditional distributions of stochastic processes without strong priors.
Linear stochastic differential equation models for panel data with unobserved variables
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Convolution-Based Converter : A Weak-Prior Approach For Modeling Stochastic Processes Based On Conditional Density Estimation
A convolutional noise-to-trajectory network trained with only an observation-matching loss is claimed to estimate conditional distributions of stochastic processes without strong priors.