Probabilistic graphical models : bayesian networks

This report conducts a review on Bayesian networks and provides a Bayesian network analysis of a dataset provided by Hewlett-Packard. The main focus of the review is on discrete Bayesian networks while there is a brief mention of Gaussian and hybrid Bayesian networks. Methodologies on constructing B...

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Main Author: Chan, Xiang Yun
Other Authors: Frederique Elise Oggier
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/148509
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1485092023-02-28T23:19:44Z Probabilistic graphical models : bayesian networks Chan, Xiang Yun Frederique Elise Oggier School of Physical and Mathematical Sciences Hewlett-Packard Frederique@ntu.edu.sg Science::Mathematics::Statistics Science::Mathematics::Analysis This report conducts a review on Bayesian networks and provides a Bayesian network analysis of a dataset provided by Hewlett-Packard. The main focus of the review is on discrete Bayesian networks while there is a brief mention of Gaussian and hybrid Bayesian networks. Methodologies on constructing Bayesian networks are included. Exploratory data analysis was conducted on the dataset before experimenting with the types of Bayesian networks that can be constructed. As the dataset contains a mix of discrete and continuous variables, few discretization methods were used on the continuous variables to produce a discrete Bayesian network. Gaussian and hybrid networks were generated as well. As a result, there were many preliminary structures to choose from. As the objective of building a network is to model the relationships among variables, structures with disjoint variables were eliminated. Six network structures were narrowed down and evaluated using network scores, expected log-loss and mean-squared errors in predictions. Inference was performed for the variables of interest. Bachelor of Science in Mathematical Sciences 2021-04-29T02:46:57Z 2021-04-29T02:46:57Z 2021 Final Year Project (FYP) Chan, X. Y. (2021). Probabilistic graphical models : bayesian networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148509 https://hdl.handle.net/10356/148509 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Mathematics::Statistics
Science::Mathematics::Analysis
spellingShingle Science::Mathematics::Statistics
Science::Mathematics::Analysis
Chan, Xiang Yun
Probabilistic graphical models : bayesian networks
description This report conducts a review on Bayesian networks and provides a Bayesian network analysis of a dataset provided by Hewlett-Packard. The main focus of the review is on discrete Bayesian networks while there is a brief mention of Gaussian and hybrid Bayesian networks. Methodologies on constructing Bayesian networks are included. Exploratory data analysis was conducted on the dataset before experimenting with the types of Bayesian networks that can be constructed. As the dataset contains a mix of discrete and continuous variables, few discretization methods were used on the continuous variables to produce a discrete Bayesian network. Gaussian and hybrid networks were generated as well. As a result, there were many preliminary structures to choose from. As the objective of building a network is to model the relationships among variables, structures with disjoint variables were eliminated. Six network structures were narrowed down and evaluated using network scores, expected log-loss and mean-squared errors in predictions. Inference was performed for the variables of interest.
author2 Frederique Elise Oggier
author_facet Frederique Elise Oggier
Chan, Xiang Yun
format Final Year Project
author Chan, Xiang Yun
author_sort Chan, Xiang Yun
title Probabilistic graphical models : bayesian networks
title_short Probabilistic graphical models : bayesian networks
title_full Probabilistic graphical models : bayesian networks
title_fullStr Probabilistic graphical models : bayesian networks
title_full_unstemmed Probabilistic graphical models : bayesian networks
title_sort probabilistic graphical models : bayesian networks
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148509
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