Building Bayesian Networks: Elicitation, Evaluation, and Learning

by Wang, Haiqin

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.