Document Type
Lecture
Publication Date
3-26-2004
Abstract
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. The 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 an application of the Markov Chain Monte Carlo numerical integration technique. For the problem at hand, the hierarchical Bayesian neural network approach is shown to be superior to the one based on hierarchical Bayesian logistic regression model as well as the classical feed forward neural networks.
Relational Format
presentation
Recommended Citation
Ghosh, Malay, "Hierarchical Bayesian Neural Networks: An Application to Prostate Cancer Study" (2004). Probability & Statistics Seminar. 60.
https://egrove.olemiss.edu/math_statistics/60
