## Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification (2010)

Citations: | 5 - 2 self |

### BibTeX

@MISC{Corani10bayesiannetworks,

author = {G. Corani and A. Antonucci and M. Zaffalon},

title = {Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification},

year = {2010}

}

### OpenURL

### Abstract

Bayesian network are powerful probabilistic graphical models for modelling uncertainty. Among others, classification represents an important application: some of the most used classifiers are based on Bayesian networks. Bayesian networks are precise models: exact numeric values should be provided for quantification. This requirement is sometimes too narrow. Sets instead of single distributions can provide a more realistic description in these cases. Bayesian networks can be generalized to cope with sets of distributions. This leads to a novel class of imprecise probabilistic graphical models, called credal networks. In particular, classifiers based on Bayesian networks are generalized to so-called credal classifiers. Unlike Bayesian classifiers, which always detect a single class as the one maximizing the posterior class probability, a credal classifier may eventually be unable to discriminate a single class. In other words, if the available information is not sufficient, credal classifiers allow for indecision between two or more classes, this providing a less informative but more robust conclusion than Bayesian classifiers.