Bayesian Belief Networks for Dementia Diagnosis and Other Applications: A Comparison of Hand-Crafting and Construction using A Novel Data Driven Technique (2008)
BibTeX
@MISC{Oteniya08bayesianbelief,
author = {Lloyd Oteniya},
title = {Bayesian Belief Networks for Dementia Diagnosis and Other Applications: A Comparison of Hand-Crafting and Construction using A Novel Data Driven Technique},
year = {2008}
}
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Abstract
The Bayesian network (BN) formalism is a powerful representation for encoding domains characterised by uncertainty. However, before it can be used it must first be constructed, which is a major challenge for any real-life problem. There are two broad approaches, namely the hand-crafted approach, which relies on a human expert, and the data-driven approach, which relies on data. The former approach is useful, however issues such as human bias can introduce errors into the model. We have conducted a literature review of the expert-driven approach, and we have cherry-picked a number of common methods, and engineered a framework to assist non-BN experts with expert-driven construction of BNs. The latter construction approach uses algorithms to construct the model from a data set. However, construction from data is provably NP-hard [45]. To solve this problem, approximate, heuristic algorithms have been proposed; in particular, algorithms that assume an order between the nodes, therefore reducing the search space. However, traditionally, this approach relies on an expert providing the order
