Journal
NEURAL NETWORKS
Volume 16, Issue 5-6, Pages 855-864Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0893-6080(03)00098-4
Keywords
survival analysis; conditioning probability estimation; neural networks; Bayesian learning; MCMC methods
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A feedforward neural network architecture aimed at survival probability estimation is presented which generalizes the standard, usually linear, models described in literature. The network builds an approximation to the survival probability of a system at a given time, conditional on the system features. The resulting model is described in a hierarchical Bayesian framework. Experiments with synthetic and real world data compare the performance of this model with the commonly used standard ones. (C) 2003 Elsevier Science Ltd. All rights reserved.
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