Applied Bayesian Networks
Steven Spansel · Aquila Digital Community (University of Southern Mississippi) · 2011
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.