Applied Mathematics Theses and Dissertations

Theses and Dissertations for the Applied Mathematics department.

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Description:: Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for …
Year:: 2019
Contributor:: Burkhart, Michael C. (creator); Harrison, Matthew (Advisor); Gidas, Basilis (Reader); Darbon, Jerome (Reader); Brown University. Department of Applied Mathematics (sponsor)
Genre:: theses
Subject:: Machine Learning; Brain-computer interfaces; Gaussian processes; Bayesian filtering; Supervised learning (Machine learning); Neural decoding; Hidden Markov models; State-space models; Neural networks (Computer science); Computational neuroscience; Discriminative probabilistic modeling; Nonparametric statistics
Collection:: Applied Mathematics; Theses and Dissertations

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