## Learning Bayesian Networks for Solving Real-World Problems (1998)

Citations: | 3 - 0 self |

### BibTeX

@MISC{Singh98learningbayesian,

author = {Moninder Singh},

title = {Learning Bayesian Networks for Solving Real-World Problems},

year = {1998}

}

### OpenURL

### Abstract

Bayesian networks, which provide a compact graphical way to express complex probabilistic relationships among several random variables, are rapidly becoming the tool of choice for dealing with uncertainty in knowledge based systems. However, approaches based on Bayesian networks have often been dismissed as unfit for many real-world applications since probabilistic inference is intractable for most problems of realistic size, and algorithms for learning Bayesian networks impose the unrealistic requirement of datasets being complete. In this thesis, I present practical solutions to these two problems, and demonstrate their effectiveness on several real-world problems. The solution proposed to the first problem is to learn selective Bayesian networks, i.e., ones that use only a subset of the given attributes to model a domain. The aim is to learn networks that are smaller, and henc...