Modeling breast tumor growth by a randomized logistic model: A computational approach to treat uncertainties via probability densities
[EN] We consider a randomized discrete logistic equation to describe the dynamics of breast tumor volume. We propose a method, that takes advantage of the principle of maximum entropy, to assign reliable distributions to model inputs (initial condition and coefficients) and sample data, respectively...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/161047 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/161047 |
| Access Level: | acceso abierto |
| Palabra clave: | Maximum entropy principle Computational model fitting Volume tumor growth Uncertainty treatment MATEMATICA APLICADA |
| Sumario: | [EN] We consider a randomized discrete logistic equation to describe the dynamics of breast tumor volume. We propose a method, that takes advantage of the principle of maximum entropy, to assign reliable distributions to model inputs (initial condition and coefficients) and sample data, respectively. Since the distributions of coefficients depend on certain parameters, we design a computational procedure to determine the above mentioned parameters using the information of the probabilistic distributions. The proposed method is successfully applied to model the breast tumor volume using real data. The approach seems to be flexible enough to be adapted to other stochastic models in future contributions. |
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