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SELECTION AND ASSESSMENT OF PHENOMENOLOGICAL MODELS OF TUMOR GROWTH

Institute for Computational Engineering and Sciences, The University of Texas at Austin, 1 University Station C0200, Austin TX 78712, USA

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ERNESTO E. PRUDENCIO

Institute for Computational Engineering and Sciences, The University of Texas at Austin, 1 University Station C0200, Austin TX 78712, USA

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, and

ANDREA HAWKINS-DAARUD

Division of Neuropathology, University of Washington, 1959 NE Pacific St., Box 357470, Seattle WA 98195, USA

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https://doi.org/10.1142/S0218202513500103Cited by:58 (Source: Crossref)

Abstract

We address general approaches to the rational selection and validation of mathematical and computational models of tumor growth using methods of Bayesian inference. The model classes are derived from a general diffuse-interface, continuum mixture theory and focus on mass conservation of mixtures with up to four species. Synthetic data are generated using higher-order base models. We discuss general approaches to model calibration, validation, plausibility, and selection based on Bayesian-based methods, information theory, and maximum information entropy. We also address computational issues and provide numerical experiments based on Markov chain Monte Carlo algorithms and high performance computing implementations.

Keywords:

AMSC: 62F07, 62F15, 65C40, 65M60, 93A30

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