Article

A simple model for learning in volatile environments

2019-07-16

Abstract excerpt

Sound principles of statistical inference dictate that uncertainty shapes learning. In this work, we revisit the question of learning in volatile environments, in which both the first and second-order statistics of observations dynamically evolve over time. We propose a new model, the volatile Kalman filter (VKF), which is based on a tractable state-space model of uncertainty and extends the Kalman filter algorith...

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Literature Corpus work: d9d4704d-d5b7-58de-9f0e-ee694c80bb9d
DOI: 10.1101/701466

Open publication

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