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Item On the usability of continuous time bayesian networks: improving scalability and expressiveness(Montana State University - Bozeman, College of Engineering, 2017) Perreault, Logan Jared; Chairperson, Graduate Committee: John SheppardThe Continuous Time Bayesian Network (CTBN) is a model capable of compactly representing the behavior of discrete state systems that evolve in continuous time. This is achieved by factoring a Continuous Time Markov Process using the structure of a directed graph. Although CTBNs have proven themselves useful in a variety of applications, adoption of the model for use in real-world problems can be difficult. We believe this is due in part to limitations relating to scalability as well as representational power and ease of use. This dissertation attempts to address these issues. First, we improve the expressiveness of CTBNs by providing procedures that support the representation of non-exponential parametric distributions. We also propose the Continuous Time Decision Network (CTDN) as a framework for representing decision problems using CTBNs. This new model supports optimization of a utility value as a function of a set of possible decisions. Next, we address the issue of scalability by providing two distinct methods for compactly representing CTBNs by taking advantage of similarities in the model parameters. These compact representations are able to mitigate the exponential growth in parameters that CTBNs exhibit, allowing for the representation of more complex processes. We then introduce another approach to managing CTBN model complexity by introducing the concept of disjunctive interaction for CTBNs. Disjunctive interaction has been used in Bayesian networks to provide significant reductions in the number of parameters, and we have adapted this concept to provide the same benefits within the CTBN framework. Finally, we demonstrate how CTBNs can be applied to the real-world task of system prognostics and diagnostics. We show how models can be built and parameterized directly using information that is readily available for diagnostic models. We then apply these model construction techniques to build a CTBN describing a vehicle system. The vehicle model makes use of some of the newly introduced algorithms and techniques, including the CTDN framework and disjunctive interaction. This extended application not only demonstrates the utility of the novel contributions presented in this work, but also serves as a template for applying CTBNs to other real-world problems.Item Factored evolutionary algorithms: cooperative coevolutionary optimization with overlap(Montana State University - Bozeman, College of Engineering, 2017) Strasser, Shane Tyler; Chairperson, Graduate Committee: John SheppardFactored Evolutionary Algorithms (FEA) define a relatively new class of evolutionary-based optimization algorithms that have been successfully applied to various problems, such as training neural networks and performing abductive inference in graphical models. FEA is unique in that it factors the function being optimized by creating subpopulations that optimize over a subset of dimensions of the function. However, unlike other optimization techniques that subdivide optimization problems, FEA encourages subpopulations to overlap with one another, allowing subpopulations to compete and share information. Although FEA has been shown to be very effective at function optimization, there is still little understanding with respect to its general characteristics. In this dissertation, we present seven results exploring the theoretical and empirical properties of FEA. First, we present a formal definition of FEA and demonstrate its relationships to other multiple population algorithms. Second, we demonstrate that FEA's success is independent of the underlying optimization algorithm by evaluating the performance of FEA using a wide variety of evolutionary- and swarm-based algorithms over single-population and non-overlapping versions. Third, we demonstrate that for a given problem, there is an optimal way to generate groups of overlapping subpopulations derived using the Markov blanket in Bayesian networks. Fourth, we establish that a class of optimization functions like NK landscapes can be mapped directly to probabilistic graphical models. Additionally, we demonstrate that factor architectures derived from Markov blankets maintain better diversity of individuals in their population. Fifth, we present a new discrete Particle Swarm Optimization (PSO) algorithm and compare its performance to competing approaches. In addition, we analyze the performance of FEA versions of discrete PSO and discover that FEA masks the poor performance of search algorithms. We show what conditions are necessary for FEA to converge and scenarios where FEA may become stuck in suboptimal regions in the search space. Finally, we explore the performance of FEA on unitation functions and discover several instances where FEA struggles to outperform single-population algorithms. These results allow us to determine which situations are appropriate for FEA when using solving real-world problems.Item New methods in computation of reaction fluxes from metabolomics data(Montana State University - Bozeman, College of Engineering, 2018) Salinas Duron, Daniel; Chairperson, Graduate Committee: Brendan MumeyChanges in cellular metabolism can be deduced from how they affect the measurable metabolites in cell samples. We provide methods to compute metabolic reaction rates from changes in measurable metabolites over time. The methods provided are intended to overcome technical challenges, such as the inapplicability of a steady state assumption, heterogeneity of samples from different donors, and the lack of targeted metabolomics data. Solutions to these challenges involve identifying metabolites constrained even under non-steady state, using components analysis to find the donor consensus, and using an integer linear program to solve a set cover variant designed to generate targeted data from untargeted data. The methods are applied on data derived from diseased articular cells. The results show that the reaction rates inferred from the incomplete data are biologically relevant, and that the minimal pathways captured ancillary processes that alternative approaches ignored. We conclude that, although the resulting rates and pathways are not conclusive, they provide useful guidance on experiments to pursue. On the experimental side, our findings have lead us to believe that osteoarthritic chondrocytes respond to compression by initiating protein synthesis, opening the possibility of physical therapy as a stimulus for cartilage regeneration.