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

Autores UPV
Año
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.