LEARNING BAYESIAN NETWORK PARAMETERS FROM SOFT DATA
Bayesian networks have been known as a powerful knowledge representation model and a computational architecture for reasoning under uncertainty. However, there are still some obstacles in applying them for practical applications. Among them are learning parameters, or the conditional probability tables from data. Much work in the field focuses on handling incomplete data, i.e. data with missing values. In this paper, we propose an approach for learning parameters of Bayesian networks from soft data, the values of which may be imprecise or uncertain, given by beliefs or confidence scales. Theoretical justification of the approach is given, and experimental results demonstrated the effectiveness of our approach.
bayesian network soft evidence parameter learning sufficient statistics