Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Swan II, Edward J.
Committee Member
Zhang, Song
Committee Member
McAnally, William H.
Date of Degree
5-11-2013
Document Type
Graduate Thesis - Open Access
Major
Computer Science
Degree Name
Master of Science
College
James Worth Bagley College of Engineering
Department
Department of Computer Science and Engineering
Abstract
Data generated by complex, computational models can provide highly accurate predictions of hydrological and hydrodynamic data in multiple dimensions. Unfortunately, however, for large data sets, running these models is often timeconsuming and computationally expensive. Thus, finding a way to reduce the running time of these models, while still producing comparable results, is of notable interest. The Inference Engine is a proposed system for doing just this. It takes previously generated model data and uses them to predict additional data. Its performance, both accuracy and running time, has been compared to the performance of the actual models, in increasingly difficult data prediction tasks, and it is able, with sufficient accuracy, to quickly predict unknown model data.
URI
https://hdl.handle.net/11668/20531
Recommended Citation
Phillips, Nate, "The Inference Engine" (2013). Theses and Dissertations. 4532.
https://scholarsjunction.msstate.edu/td/4532