As a compact graphical framework for representation of multivariate probability
distributions, Bayesian networks are widely used for efficient reasoning under
uncertainty in a variety of applications, from medical diagnosis to computer
troubleshooting and airplane fault isolation. However, construction of Bayesian
networks is often considered the main difficulty when applying this framework
to real-world problems. In real world domains, Bayesian networks are often built by knowledge engineering approach. Unfortunately, eliciting knowledge from domain experts is
a very time-consuming process, and could result in poor-quality graphical
models when not performed carefully. Over the last decade, the research focus
is shifting more towards learning Bayesian networks from data, especially with
increasing volumes of data available in various applications, such as
biomedical, internet, and e-business, among others.
Aiming at solving the bottle-neck problem of building Bayesian network models, this
research work focuses on elicitation, evaluation and learning Bayesian
networks. Specifically, the contribution of this dissertation involves the research in the following five areas:
a) graphical user interface tools for
efficient elicitation and navigation of probability distributions, b) systematic and objective evaluation of elicitation schemes for probabilistic models, c)
valid evaluation of performance robustness, i.e., sensitivity, of Bayesian networks,
d) the sensitivity inequivalent characteristic of Markov equivalent networks, and the appropriateness of using sensitivity for model selection in learning Bayesian networks,
e) selective refinement for learning probability parameters of Bayesian networks from limited data with availability of expert knowledge. In addition, an efficient algorithm for fast sensitivity analysis is developed based on relevance reasoning technique. The implemented algorithm runs very fast and makes d) and e) more affordable for real domain practice.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-12062004-114342 |
| Date | 15 October 2007 |
| Creators | Wang, Haiqin |
| Contributors | Marek Druzdzel, Micheal Lewis, Irina Rish, Gregory Cooper |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-12062004-114342/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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