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Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection
(2018)
This thesis naturally divides itself into two sections. The first two chapters concernthe
development of Bayesian semi-parametric models for arrival times. Chapter 2considers
Bayesian inference for a Gaussian process modulated ...
Tailored Scalable Dimensionality Reduction
(2018)
Although there is a rich literature on scalable methods for dimensionality reduction,
the focus has been on widely applicable approaches which, in certain applications,
are far from optimal or not even applicable. Dimensionality ...
Advances in Bayesian Modeling of Protein Structure Evolution
(2018)
This thesis contributes to a statistical modeling framework for protein sequence and
structure evolution. An existing Bayesian model for protein structure evolution is
extended in two unique ways. Each of these model extensions ...
Gaussian Process-Based Models for Clinical Time Series in Healthcare
(2018)
Clinical prediction models offer the ability to help physicians make better data-driven
decisions that can improve patient outcomes. Given the wealth of data available with
the widespread adoption of electronic health records, ...
Finite Sample Bounds and Path Selection for Sequential Monte Carlo
(2018)
Sequential Monte Carlo (SMC) samplers have received attention as an alternative to
Markov chain Monte Carlo for Bayesian inference problems due to their strong empirical
performance on difficult multimodal problems, natural ...
Topic Modeling for Inferring Brain States from Electroencephalography (EEG) Signals
(2018)
Inferring brain states from EEG signals allows for the management of sleep disorders
and brain diseases by providing an insight into the electrophysiological state of
the brain. We explore the use of topic modeling – which ...
Towards Better Representations with Deep/Bayesian Learning
(2018)
Deep learning and Bayesian Learning are two popular research topics in machine learning.
They provide the flexible representations in the complementary manner. Therefore,
it is desirable to take the best from both fields. ...
Algorithms with Applications to Anthropology
(2018)
In this dissertation, we investigate several problems in shape analysis. We start
by discussing the shape matching problem. Given that homeomorphisms of shapes are
computed in practice by interpolating sparse correspondence, ...
Compressive Sensing in Transmission Electron Microscopy
(2018)
Electron microscopy is one of the most powerful tools available in observational science.
Magnifications of 10,000,000x have been achieved with picometer precision. At this
high level of magnification, individual atoms are ...
Bayesian Parameter Estimation for Relativistic Heavy-ion Collisions
(2018)
I develop and apply a Bayesian method for quantitatively estimating properties of
the quark-gluon plasma (QGP), an extremely hot and dense state of fluid-like matter
created in relativistic heavy-ion collisions.The QGP cannot ...