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 a posteriori soft correction. Furthermore, we show that this accuracy quickly asymptotes and begins to show diminishing returns on large data sets.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Spansel, Steven David"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Masters Thesis","degree_discipline":"Computing","degree_department":null,"school":null,"contributors":["Louise Perkins"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-01T07:00:00Z","date_published":"2011-05-01T07:00:00Z","updated_at":"2026-07-24T05:45:06Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aquila.usm.edu/masters_theses/526","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Louise Perkins"]},{"key":"dc:creator","label":"Author","values":["Spansel, Steven David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-11-16T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computing"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://aquila.usm.edu/masters_theses/526"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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 a posteriori soft correction. Furthermore, we show that this accuracy quickly asymptotes and begins to show diminishing returns on large data sets.
"]},{"key":"dc:title","label":"Title","values":["Applied Bayesian Networks"]}]}],"canonical_facts":{"dc:contributor":["Louise Perkins"],"dc:creator":["Spansel, Steven David"],"dc:date.available":["2018-11-16T08:00:00Z"],"dc:description.abstract":["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 a posteriori soft correction. Furthermore, we show that this accuracy quickly asymptotes and begins to show diminishing returns on large data sets.
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