Malay Ghosh, University of Florida
Hierarchical Bayesian Neural Networks: An Application to Prostate
Cancer Study
Prostate cancer is one of the most common cancers in
American men. Management depends on the staging of prostate
cancer. Only cancers that are confined to organs of origin are
potentially curable. This paper considers a hierarchical Bayesian
neural network approach for posterior prediction probabilities of
certain features indicative of non-organ confined prostate cancer. The
Bayesian procedure is implemented by the Markov Chain Monte Carlo
numerical integration technique. For the problem at hand, the neural
network method is shown to be superior to one based on a hierarchical
Bayesian logistic regression model.