This dissertation focuses on New Amsterdam, the small port town at the tip of Manhattan Island that became the capital for the Dutch colony of New Netherland. It addresses two of the most entrenched stereotypes regarding ...
<p>In his Dispatches, Michael Herr quotes the gonzo photojournalist Tim Page: "Take the glamour out of war! I mean, how the bloody hell can you do that?[...] Ohhhh, war is good for you, you can't take the glamour out of ...
<p>Bacteria communicate, coordinate, and cooperate as a population and this `social' behavior is key to their proliferation. Quorum sensing (QS) is the cell-cell communication mechanism by which bacteria sense their ...
<p>The activation and maintenance of plant immune responses require a significant amount of energy because they are accompanied by massive transcriptional reprogramming. Spurious activation of plant defense machinery can ...
<p>The tenacity by which barnacles adhere has sparked a long history of scientific investigation into their adhesive mechanisms. To adhere, barnacles utilize proteinaceous cement that rapidly polymerizes and forms adhesive ...
<p>Online Social Network (OSN) services such as Facebook and Google+ are fun and useful. Hundreds of millions of users rely on these services and third-party applications to process and share personal data such as friends ...
<p>Although Charles Baudelaire's poetry was censored in part for his graphic representations of death, for Baudelaire himself, death was the ultimate censorship. He grappled with its limitations of the possibility of ...
<p>This thesis is about Bayesian approaches for handling multiplicity. It considers three main kinds of multiple-testing scenarios: tests of exchangeable experimental units, tests for variable inclusion in linear regresson ...
<p>Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally ...
<p>Time series modeling faces increasingly high-dimensional problems in many scientific areas. Lack of relevant, data-based constraints typically leads to increased uncertainty in estimation and degradation of predictive ...
<p>The concept of sparseness is harnessed to learn a low dimensional representation of high dimensional data. This sparseness assumption is exploited in multiple ways. In the Bayesian Elastic Net, a small number of correlated ...
<p>Urbanization causes myriad changes in watershed processes, ultimately disrupting the structure and function of stream ecosystems. Urban development introduces contaminants (human waste, pesticides, industrial chemicals). ...
<p>This thesis examines causal inference related topics involving intermediate variables, and uses Bayesian methodologies to advance analysis capabilities in these areas. First, joint modeling of outcome variables with ...
<p>Multivariate or high-dimensional data with mixed types are ubiquitous in many fields of studies, including science, engineering, social science, finance, health and medicine, and joint analysis of such data entails both ...
<p>This dissertation is devoted to modeling complex data from the</p><p>Bayesian perspective via constructing priors with latent structures.</p><p>There are three major contexts in which this is done -- strategies for</p><p>the ...
<p>Applied studies in multiple areas involving spatial and dynamic systems increasingly challenge our modelling and computational abilities as data volumes increase, and as spatial and temporal scales move to increasingly ...
<p>The Bayesian approach to model selection allows for uncertainty in both model specific parameters and in the models themselves. Much of the recent Bayesian model uncertainty literature has focused on defining these ...
<p>Modelling and inference with higher-dimensional variables, including studies in multivariate time series analysis, raise challenges to our ability to ``scale-up'' statistical approaches that involve both modelling and ...
<p>The dissertation focuses on solving some important theoretical and methodological problems associated with Bayesian modeling of infinite dimensional `objects', popularly called nonparametric Bayes. The term `infinite ...
<p>Identifying a lower-dimensional latent space for representation of high-dimensional observations is of significant importance in numerous biomedical and machine learning applications. In many such applications, it is ...