Abstract
dc:description.abstract<p>A Bayesian Network is a stochastic graphical model that can be used to maintain and propagate conditional probability tables among its nodes. Here, we use a Bayesian Network to model results from a numerical riverine model. We develop an discretization optimization algorithm that improves efficiency and concurrently increases the overall accuracy of the resulting network. We measure accuracy using a new prediction accuracy criteria that includes an <em>a posteriori</em> soft correction. Furthermore, we show that this accuracy quickly asymptotes and begins to show diminishing returns on large data sets.</p>
Degree
thesis:*- Name thesis:degree_name
- Master of Science (MS)
- Level thesis:degree_level
- Masters Thesis
- Discipline thesis:degree_discipline
- Computing
- Year dc:date.available
- 2011
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Spansel, Steven David
- Contributors dc:contributor
-
- Louise Perkins
Identifiers
dc:identifier.*- Repository record dc:identifier
- https://aquila.usm.edu/masters_theses/526
- OAI identifier oai:identifier
- oai:aquila.usm.edu:masters_theses-1604