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Bayesian prediction for flowgraph models with covariates. An application to bladder carcinoma.

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Revista

Journal of Computational and Applied Mathematics

Abstract

Statistical Flowgraph Models are an efficient tool to model multi-state stochastic processes. They support both frequentist and Bayesian approaches. Inclusion of covariates is also available. In this paper we propose an easy way to perform a Bayesian approach with covariates. Results are presented with an application to bladder carcinoma data.