Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis

Gavin C. Cawley, Nicola L. C. Talbot, Gareth J. Janacek, Mike W. Peck

Research output: Chapter in Book/Report/Conference proceedingChapter


Survival analysis is a branch of statistics concerned with the time elapsing before “failure”, with diverse applications in medical statistics and the analysis of the reliability of electrical or mechanical components. In this paper we introduce a parametric accelerated life survival analysis model based on kernel learning methods that, at least in principal, is able to learn arbitrary dependencies between a vector of explanatory variables and the scale of the distribution of survival times. The proposed kernel survival analysis method is then used to model the growth domain of Clostridium botulinum, that is the food processing and storage conditions permitting the growth of this foodborne microbial pathogen, leading to the production of the neurotoxin responsible for botulism. A Bayesian training procedure, based on the evidence framework, is used for model selection and to provide a credible interval on model predictions. The kernel survival analysis models are found to be more accurate than models based on more traditional survival analysis techniques, but also suggest a risk assessment of the foodborne botulism hazard would benefit from the collection of additional data.
Original languageEnglish
Title of host publicationDeterministic and Statistical Methods in Machine Learning
EditorsJoab Winkler, Mahesan Niranjan, Neil Lawrence
PublisherSpringer Berlin / Heidelberg
Number of pages19
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg

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