It is well-known that the observation of a variable in a Bayesian network can affect the
effective connectivity of the network, which in turn affects the efficiency of inference.
Unfortunately, the observed variables may not be known until runtime, which limits the
amount of compile-time optimization that can be done in this regard. This thesis considers
how to improve inference when users know the likelihood of a variable being observed. It
demonstrates how these probabilities of observation can be exploited to improve existing
heuristics for choosing elimination orderings for inference. Empirical tests over a set of
benchmark networks using the Variable Elimination algorithm show reductions of up to
50% and 70% in multiplications and summations, as well as runtime reductions of up to
55%. Similarly, tests using the Elimination Tree algorithm show reductions by as much as
64%, 55%, and 50% in recursive calls, total cache size, and runtime, respectively. / xi, 88 leaves : ill. ; 29 cm
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:ALU.w.uleth.ca/dspace#10133/3457 |
| Date | January 2013 |
| Creators | Mousumi, Fouzia Ashraf |
| Contributors | Grant, Kevin, Wismath, Stephen |
| Publisher | Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, Arts and Science, Department of Mathematics and Computer Science |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_CA |
| Detected Language | English |
| Type | Thesis |
| Relation | Thesis (University of Lethbridge. Faculty of Arts and Science) |
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