A Python implementation of graphical models

Date
2010-03
Authors
Gouws, Almero
Journal Title
Journal ISSN
Volume Title
Publisher
Stellenbosch : University of Stellenbosch
Abstract
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.
Description
Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010.
Keywords
Graphical models, Bayesian networks, Markov random fields, Dissertations -- Electronic engineering, Theses -- Electronic engineering, GrMPy
Citation