Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: In this thesis we present GrMPy, a library of classes and functions implemented in Python, designed
for implementing graphical models. GrMPy supports both undirected and directed models, exact
and approximate probabilistic inference, and parameter estimation from complete and incomplete
data. In this thesis we outline the necessary theory required to understand the tools implemented
within GrMPy as well as provide pseudo-code algorithms that illustrate how GrMPy is implemented. / AFRIKAANSE OPSOMMING: In hierdie verhandeling bied ons GrMPy aan,'n biblioteek van klasse en funksies wat Python geim-
plimenteer word en ontwerp is vir die implimentering van grafiese modelle. GrMPy ondersteun beide
gerigte en ongerigte modelle, presies eenbenaderde moontlike gevolgtrekkings en parameterskat-
tings van volledige en onvolledige inligting. In hierdie verhandeling beskryf ons die nodige teorie wat
benodig word om die hulpmiddels wat binne GrMPy geimplimenteer word te verstaan sowel as die
pseudo-kodealgoritmes wat illustreer hoe GrMPy geimplimenteer is.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/4147 |
| Date | 03 1900 |
| Creators | Gouws, Almero |
| Contributors | Herbst, B. M., University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 128 p. : ill. |
| Rights | University of Stellenbosch |
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