Browsing by Subject "Bayesian Analysis"
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Item Bayesian Analysis of Transposon Mutagenesis Data(2012-07-16) DeJesus, Michael A.Determining which genes are essential for growth of a bacterial organism is an important question to answer as it is useful for the discovery of drugs that inhibit critical biological functions of a pathogen. To evaluate essentiality, biologists often use transposon mutagenesis to disrupt genomic regions within an organism, revealing which genes are able to withstand disruption and are therefore not required for growth. The development of next-generation sequencing technology augments transposon mutagenesis by providing high-resolution sequence data that identifies the exact location of transposon insertions in the genome. Although this high-resolution information has already been used to assess essentiality at a genome-wide scale, no formal statistical model has been developed capable of quantifying significance. This thesis presents a formal Bayesian framework for analyzing sequence information obtained from transposon mutagenesis experiments. Our method assesses the statistical significance of gaps in transposon coverage that are indicative of essential regions through a Gumbel distribution, and utilizes a Metropolis-Hastings sampling procedure to obtain posterior estimates of the probability of essentiality for each gene. We apply our method to libraries of M. tuberculosis transposon mutants, to identify genes essential for growth in vitro, and show concordance with previous essentiality results based on hybridization. Furthermore, we show how our method is capable of identifying essential domains within genes, by detecting significant sub-regions of open-reading frames unable to withstand disruption. We show that several genes involved in PG biosynthesis have essential domains.Item Dynamic Agent Based Modeling Using Bayesian Framework for Addressing Intelligence Adaptive Nuclear Nonproliferation Analysis(2014-10-03) Elmore, Royal ARealistically, no two nuclear proliferating or defensive entities are exactly identical; Agent Based Modeling (ABM) is a computational methodology addressing the uniqueness of those facilitating or preventing nuclear proliferation. The modular Bayesian ABM Nonproliferation Enterprise (BANE) tool has been developed at Texas A &M University for nuclear nonproliferation analysis. Entities engaged in nuclear proliferation cover a range of activities and fall within proliferating, defensive, and neutral agent classes. In BANE proliferating agents pursue nuclear weapons, or at least a latent nuclear weapons capability. Defensive nonproliferation agents seek to uncover, hinder, reverse, or dismantle any proliferation networks they discover. The vast majority of agents are neutral agents, of which only a small subset can significantly enable proliferation. BANE facilitates intelligent agent actions by employing entropy and mutual information for proliferation pathway determinations. Factors including technical success, resource expenditures, and detection probabilities are assessed by agents seeking optimal proliferation postures. Coupling ABM with Bayesian analysis is powerful from an omniscience limitation perspective. Bayesian analysis supports linking crucial knowledge and technology requirements into relationship networks for each proliferation category. With a Bayesian network, gaining information on proliferator actions in one category informs defensive agents where to expend limited counter-proliferation impeding capabilities. Correlating incomplete evidence for pattern recognition in BANE using Bayesian inference draws upon technical supply side proliferation linkages grounded in physics. Potential or current proliferator security, economic trajectory, or other factors modify demand drivers for undertaking proliferation. Using Bayesian inference the coupled demand and supply proliferation drivers are connected to create feedback interactions. Verification and some validation for BANE is performed using scenarios and historical case studies. Restrictive export controls, swings in global soft power affinity, and past proliferation program assessments for entities ranging from the Soviet Union to Iraq demonstrates BANE?s flexibility and applicability. As a newly developed tool, BANE has room for future contributions from computer science, engineering, and social scientists. Through BANE the framework exists for detailed nonproliferation expansion into broader weapons of mass effect analysis; since, nuclear proliferation is but one option for addressing international security concerns.Item Productivity prediction model based on Bayesian analysis and productivity console(Texas A&M University, 2005-08-29) Yun, Seok JunSoftware project management is one of the most critical activities in modern software development projects. Without realistic and objective management, the software development process cannot be managed in an effective way. There are three general problems in project management: effort estimation is not accurate, actual status is difficult to understand, and projects are often geographically dispersed. Estimating software development effort is one of the most challenging problems in project management. Various attempts have been made to solve the problem; so far, however, it remains a complex problem. The error rate of a renowned effort estimation model can be higher than 30% of the actual productivity. Therefore, inaccurate estimation results in poor planning and defies effective control of time and budgets in project management. In this research, we have built a productivity prediction model which uses productivity data from an ongoing project to reevaluate the initial productivity estimate and provides managers a better productivity estimate for project management. The actual status of the software project is not easy to understand due to problems inherent in software project attributes. The project attributes are dispersed across the various CASE (Computer-Aided Software Engineering) tools and are difficult to measure because they are not hard material like building blocks. In this research, we have created a productivity console which incorporates an expert system to measure project attributes objectively and provides graphical charts to visualize project status. The productivity console uses project attributes gathered in KB (Knowledge Base) of PAMPA II (Project Attributes Monitoring and Prediction Associate) that works with CASE tools and collects project attributes from the databases of the tools. The productivity console and PAMPA II work on a network, so geographically dispersed projects can be managed via the Internet without difficulty.Item Seismic fragility estimates for corroded reinforced concrete bridge structures with two-column bents(2009-05-15) Zhong, JinquanTo assess the losses associated with future earthquakes, seismic vulnerability functions are commonly used to correlate the damage or loss of a structure to the level of seismic intensity. A common procedure in seismic vulnerability assessment is to estimate the seismic fragility, which is defined as the conditional probability that a structure fails to meet the specific performance level for given level of seismic intensity. This dissertation proposes a methodology to estimate the fragility of corroded reinforced concrete (RC) bridges with two-column bents subject to seismic excitation. Seismic fragility functions are first developed for the RC bridges with two-column bents. All available information from science/engineering laws, numerical analysis, laboratory experiments, and field measurements has been used to construct the proper form of the fragility functions. The fragility functions are formulated, at the individual column, bent, and bridge levels, in terms of the spectral acceleration and the ratio between the peak ground velocity and the peak ground acceleration. The developed fragility functions properly account for the prevailing uncertainties in fragility estimation. The probabilistic capacity and demand models are then combined with the probabilistic models for chloride-induced corrosion and the time-dependent corrosion rate. The fragility estimates for corroded RC bridges incorporates the uncertainties in the parameters of capacity and demand models, and the inexactness (or model error) in modeling the material deterioration, structural capacity, and seismic demands. The proposed methodology is illustrated by developing the fragility functions for an example RC bridge with 11 two-column bents representing current construction in California. The developed fragility functions provide valuable information to allocate and spend available funds for the design, maintenance, and retrofitting of structures and networks. This study regarding the vulnerability of corroding RC bridges will be of direct value to those making decisions about the condition assessment, residual life, and the ability of lifeline structures to withstand future seismic demands.