Shane Strasser

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Bayesian networks are probabilistic graphical models that have proven to be able to handle uncertainty in many real-world applications. One key issue in learning Bayesian networks is parameter estimation, i.e., learning the local conditional distributions of each variable in the model. While parameter estimation can be performed efficiently when complete(More)
In this paper we propose several approximation algorithms for the problems of full and partial abductive inference in Bayesian belief networks. Full abductive inference is the problem of finding the k most probable state assignments to all non-evidence variables in the network while partial abductive inference is the problem of finding the k most probable(More)
—Bayesian networks are powerful probabilistic models that have been applied to a variety of tasks. When applied to classification problems, Bayesian networks have shown competitive performance when compared to other state-of-the-art classifiers. However, structure learning of Bayesian networks has been shown to be NP-Hard. In this paper, we propose a novel(More)
Design pattern languages have started to gain more attention by providing the ability to specify instances of patterns. The Role Based Metamodeling Language (RBML) is a visually oriented language defined in terms of a specialization of the UML meta-model that is used to verify and specify generic or domain specific design patterns. The evolution of(More)
—We created an application that facilitates improved knowledge discovery from aircraft maintenance data by transforming transactional database records into ontology-based event graphs, and then providing a filterable visualization of event sequences through time. We developed OWL ontologies based on formally defined IEEE standards, and use these ontologies(More)
—In model-based diagnostic algorith assumed that the model is correct. If the model is incorrect, the diagnostic algorithm may diagnose the wrong fault, which can be costly and time consuming. Using past maintenance events, one should be able to make corrections to the model in order for diagnostic algorithm to correctly diagnosis faults. In this paper, a(More)
Particle Swarm Optimization (PSO) has been shown to perform very well on a wide range of optimization problems. One of the drawbacks to PSO is that the base algorithm assumes continuous variables. In this paper, we present a version of PSO that is able to optimize over discrete variables. This new PSO algorithm, which we call Integer and Categorical PSO(More)
Factored Evolutionary Algorithms (FEA) have proven to be fast and efficient optimization methods, often outperforming established methods using single populations. One restriction to FEA is that it requires a central communication point between all of the factors, making FEA difficult to use in completely distributed settings. The Distributed Factored(More)
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