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

B. García-Mora ^[1] ; C. Santamaría ^[1] ; G. Rubio ^[1] ; J.L. Pontones ^[2]
1. [1] Universidad Politécnica de Valencia
  
  Universidad Politécnica de Valencia
  
  Valencia, España
2. [2] Hospital Universitario La Fe
  
  Hospital Universitario La Fe
  
  Valencia, España
Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 291, Nº 1 (1 January 2016), 2016, págs. 85-93
Idioma: inglés
DOI: 10.1016/j.cam.2015.03.045
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Resumen
- 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.