Bayesian Network Development

Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They are a fundamental probabilistic representation mechanism that subsumes a great variety of other stochastic modeling methods, such as hidden Markov models, stochastic dynamic systems. Bayesian networks, in principle, make it possible to build large, complex stochastic models from standard components. Development methodologies for Bayesian networks have been introduced based on software engineering methodologies. However, this is complicated by the significant differences between the crisp, logical foundations of modern software and the fuzzy, empirical nature of stochastic modeling. Conversely, software engineering would benefit from better integration with Bayesian networks, so that uncertainty and stochastic inference can be introduced in a more systematic and formal manner than it is now. In this paper, Bayesian networks and stochastic inference are briefly introduced, and the development of Bayesian networks is compared with the development of objectoriented software. The challenges involved in Bayesian network development are then discussed