Testing Distributional Granger Causality With Entropic Optimal Transport
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley +1 more source
A Deep-Learning-Based Health Indicator Constructor Using Kullback-Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures. [PDF]
Sensors (Basel), 2022 Nguyen TK, Ahmad Z, Kim JM.
europepmc +1 more source
Learning Based Non-rigid Multi-modal Image Registration Using Kullback-Leibler Divergence [PDF]
, 2005 Christoph Guetter +3 more
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Is A Little Learning Dangerous?
Noûs, EarlyView.ABSTRACT I argue that a little learning is often dangerous even for ideal reasoners who are operating in extremely simple scenarios and know all the relevant facts about how the evidence is generated. More precisely, I show that, on many plausible ways of assigning value to a credence in a hypothesis H, ideal Bayesians should sometimes expect other ...
Bernhard Salow
wiley +1 more source
Construction of an individualized brain metabolic network in patients with advanced non-small cell lung cancer by the Kullback-Leibler divergence-based similarity method: A study based on 18F-fluorodeoxyglucose positron emission tomography. [PDF]
Front Oncol, 2023 Yu J +6 more
europepmc +1 more source
Computing the Kullback-Leibler Divergence between two Weibull Distributions [PDF]
, 2013 Christian Bauckhage
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Tracking changes using Kullback-Leibler divergence for the continual learning [PDF]
, 2022 Sebastián Basterrech, Michał Woźniak
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Topics in Cognitive Science, EarlyView.Abstract Recent studies suggest that learners who are asked to predict the outcome of an event learn more than learners who are asked to evaluate it retrospectively or not at all. One possible explanation for this “prediction boost” is that it helps learners engage metacognitive reasoning skills that may not be spontaneously leveraged, especially for ...
Joseph A. Colantonio +4 more
wiley +1 more source
Ratio Divergence Learning Using Target Energy in Restricted Boltzmann Machines: Beyond Kullback--Leibler Divergence Learning [PDF]
Yuichi Ishida +4 more
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A Metrological Hyperspectral Texture Similarity Measure using Kullback-Leibler Divergence
, 2022 Chu, Rui Jian +3 more
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