The marine ecosystem provides a broad spectrum of services for a wide array of user groups. Such broad use of marine resources often results in use conflicts between ecosystem elements and human interests. To help mitigate these conflicts, the Bayesian Analysis for Spatial Siting (BASS) tool was developed to incorporate...
Machine learning encompasses probabilistic and statistical techniques that can build models from large quantities of extensional information (examples) with minimal dependence on intensional information (domain knowledge). This focus of machine learning is reflected in the never-ending quest for "off-the-shelf" classifiers. To generalize to unseen data, however, we must make use...
This thesis describes the application of Bayesian networks for monitoring and
diagnosis of a multi-stage manufacturing process, specifically a high speed production
part at Hewlett Packard. Bayesian network "part models" were designed to represent
individual parts in-process. These were combined to form a "process model", which is a
Bayesian network...
Logic Sampling, Likelihood Weighting and AIS-BN are three variants of stochastic sampling, one class of approximate inference for Bayesian networks. We summarize the ideas underlying each algorithm and the relationship among them. The results from a set of empirical experiments comparing Logic Sampling, Likelihood Weighting and AIS-BN are presented. We...
Gibbs sampling method is an important tool used in parameter estimation for many probabilistic models. Specifically, for many scenarios, it is difficult to generate high-dimensional data samples from its joint distribution. The Gibbs sampling provides a way to draw high-dimensional data via the conditional distributions which are typically easier to...