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Found 5 papers, 1 papers with code
Deep Conditional Generative Learning: Model and Error Analysis
We introduce an Ordinary Differential Equation (ODE) based deep generative method for learning a conditional distribution, named the Conditional Follmer Flow.
Efficiently handling constraints with Metropolis-adjusted Langevin algorithm
In this study, we investigate the performance of the Metropolis-adjusted Langevin algorithm in a setting with constraints on the support of the target distribution.
Testing the martingale difference hypothesis in high dimension
In this paper, we consider testing the martingale difference hypothesis for high-dimensional time series.
Modelling matrix time series via a tensor CP-decomposition
We consider to model matrix time series based on a tensor CP-decomposition.
Culling the herd of moments with penalized empirical likelihood
Models defined by moment conditions are at the center of structural econometric estimation, but economic theory is mostly agnostic about moment selection.
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