Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2011 - 20162

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    A META-DATA INFORMED EXPERT JUDGMENT AGGREGATION AND CALIBRATION TECHNIQUE
    (2016) Feldman, Ellis; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Policy makers use expert judgment opinions elicited from experts as probability distributions, quantiles or point estimates, as inputs to decisions that may have economic or life and death impacts. While challenges in estimating probabilities in general have been studied, research that distinguished between non-probabilistic, i.e., physical, variables and probabilistic variables specifically in the context of meta-data based expert judgment aggregation techniques, and the errors associated with the predictions developed from such variables, was not identified. This research demonstrated that for two combined expert judgment meta-data bases, the distinction between physical and probabilistic variables was significant in terms of the extent of multiplicative error between elicited medians and realized values both before and after aggregation. The distinction also impacts the widths of bounds around aggregated point estimates. The research compared nine methods of aggregating estimates and obtaining calibrated bounds, including ones based on alpha stable distributions, quantile regression, and a Bayesian model. Simple parametric distributions were also fit to the meta-data. Methods were compared against criteria including accuracy, bounds coverage and width, sensitivity to outliers, and complexity. No single method dominated all criteria for either variable type. The research investigated sensitivity of results to level of realized value for a variable, such as infrequent events for probabilistic variables, as well as sensitivity of results to number of experts.
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    USING AND MANIPULATING PROBABILISTIC CONNECTIVITY IN SOCIAL NETWORKS
    (2011) DuBois, Thomas M.; Srinivasan, Aravind; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Probabilistic connectivity problems arise naturally in many social networks. In particular the spread of an epidemic across a population and social trust inference motivate much of our work. We examine problems where some property, such as an infection or influence, starts from some initially seeded set of nodes and every affected node transmits the property to its neighbors with a probability determined by the connecting edge. Many problems in this area involve connectivity in a random- graph - the probability of a node being affected is the probability that there is a path to it in the random-graph from one of the seed nodes. We may wish to aid, disrupt, or simply monitor this connectivity. In our core applications, public health officials wish to minimize an epidemic's spread over a population, and connectivity in a social network suggests how closely tied its users are. In support of these and other applications, we study several combinatorial optimization problems on random-graphs. We derive algorithms and demonstrate their effectiveness through simulation, mathematical proof, or both.