In LTI systems with moral hazard, the optimal incentive payment after a fixed horizon is given by a likelihood-ratio hypothesis test between the two controller choices.
A method to integrate and classify normal distributions
2 Pith papers cite this work. Polarity classification is still indexing.
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NDIS lemma computes closed-form hockey-stick divergence δ(ε) between arbitrary multivariate Gaussians and is applied to obtain tighter privacy for random projection.
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Moral Hazard in LTI Dynamics: A Hypothesis Testing Approach
In LTI systems with moral hazard, the optimal incentive payment after a fixed horizon is given by a likelihood-ratio hypothesis test between the two controller choices.
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The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning
NDIS lemma computes closed-form hockey-stick divergence δ(ε) between arbitrary multivariate Gaussians and is applied to obtain tighter privacy for random projection.