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Business Decision Insight With Causal Bayesian Networks

Balmer, Mark (2017) Business Decision Insight With Causal Bayesian Networks. MSc by research thesis, University of York.

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Causal Bayesian Networks are a widely recognised tool for modelling the uncer- tainty of a wide range of processes, particularly when the nature of how different factors influence each other. The practice of utilising Causal Bayesian Network is now becoming a growing trend for business that want to fully understand the demands im- posed on them, and how best to adapt their business in order to be successful. When designing and building a Causal Bayesian Network, it is often necessary to consult with domain experts for information about the shape of the model but also the defini- tion of how the causal factors influence others. The definition of these influences can require the specification of a large volume of probability distributions, even if a lot of evidential data is available for analysis. Whilst the definition of the structure of the model can be a relatively simple task for a domain expert, providing the probability distributions is a much more difficult task. In this thesis I discuss a method whereby, given a model structure, a domain expert can provide simple descriptive meta-data so that a hypothetical probability distribution can be generated for the discrete model variables.

Item Type: Thesis (MSc by research)
Related URLs:
Academic Units: The University of York > Computer Science (York)
Depositing User: Mr Mark Balmer
Date Deposited: 04 May 2018 16:18
Last Modified: 04 May 2018 16:18
URI: http://etheses.whiterose.ac.uk/id/eprint/20252

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