Bayesian inductively learned modules for safety critical systems
Fieldsend, Jonathan E.
Bailey, Trevor C.
Everson, Richard M.
Krzanowski, Wojtek J.
Computing Science and Statisics Vol. 35
Interface Foundation of North America, Inc.
This work examines the use of Bayesian inductively learned software modules for safety critical systems. Central to the safety critical application is the desire to generate confidence measures associated with predictions. This is achieved in this study by casting the problem in a Bayesian formulation, and is implemented using reversible jump Markov Chain Monte Carlo (RJ-MCMC). We use conventional and novel classification architectures, including logistic discriminants, probabilistic k-nn and radial basis function networks. Results from these methods are illustrated on real life critical systems, including medical trauma data. We report results on the trade-off between model complexity and the width of the posterior predictive probability.
35th Symposium on the Interface: Computing Science and Statistics 2003: Security and Infrastructure Protection (Salt Lake City, Utah, USA, 12-15 March 2003)
Place of publication
Fairfax Station, VA