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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Description:: Electroencephalography (EEG) signals are created by electrical currents from pyramidal neurons in the outer layer of the brain. These signals are easy to obtain and …
Year:: 2020
Contributor:: Pugliese, Sarah M. (creator); Jones, Stephanie (thesis advisor); Harrison, Matthew (reader); Brown University. Applied Mathematics (sponsor)
Genre:: theses
Subject:: Dendrites--Mathematical models; Calcium channels; Electroencephalography; Computational neuroscience; Neurons--Mathematical models
Language:: English
Collection:: Applied Mathematics; Theses and Dissertations

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Description:: In many real-world applications, e.g., brain imaging and or weather patterns, data are captured over particular periods or intervals, which we call time series. Time …
Year:: 2020
Contributor:: Cao, Xuefei (creator); Sandstede, Björn (Advisor); Luo, Xi (Reader); Harrison, Matthew (Reader); Brown University. Department of Applied Mathematics (sponsor)
Genre:: theses
Subject:: Applied mathematics; Statistics; fmri; Computational neuroscience
Collection:: Applied Mathematics; Theses and Dissertations

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