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Autor: =Maiti, Tapabrata

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Autor:	Ghosh, Malay ; Maiti, Tapabrata ; Kim , Dalho ; Chakraborty, Sounak ; Tewari, Ashutosh
Título:	Hierarchical Bayesian Neural Networks: An Application to a Prostate Cancer Study
Páginas/Colación:	pp. 601 - 608
Url:	http://oberon.asa.catchword.org/vl=5518955/cl=25/nw=1/rpsv/cw/asa/01621459/v99n467/s7/p601

Journal of the American Statistical Association Vol. 99, no. 467 September 2004

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Resumen

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 article 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 approach based on hierarchical Bayesian logistic regression model as well as the classical feedforward neural networks.

UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno

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