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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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Description:: Hidden Markov models are used in countless signal processing problems, and the associated nonlinear filtering algorithms are used to obtain posterior distributions for the hidden …
Year:: 2010
Contributor:: Papanicolaou, Andrew L (creator); Rozovsky, Boris (Director); Dafermos, Constantine (Reader); Karniadakis, George (Reader); Brown University. Applied Mathematics (sponsor)
Genre:: theses
Subject:: filtering; Hidden Markov models
Language:: English
Collection:: Applied Mathematics; Theses and Dissertations

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Description:: The neocortical-entorhinal-hippocampal circuit has been shown to play an important role in memory formation and consolidation. Furthermore, the entorhinal cortex (EC) and the hippocampus are …
Year:: 2011
Contributor:: McFarland, James M (creator); Mehta, Mayank (Director); Stein, Derek (Director); Stein, Derek (Reader); Ling, Xinsheng (Reader); Brown University. Physics (sponsor)
Genre:: theses
Subject:: UP-DOWN states; hippocampus; entorhinal cortex; Sea horses; Hidden Markov models
Language:: English
Collection:: Physics; Theses and Dissertations

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