On some descriptive and predictive methods for the dynamics of cancer growth

DOI10.6092/issn.1973-2201/6096zbMath1473.62375OpenAlexW2342646370MaRDI QIDQ5163401

Iulian T. Vlad, Jorge Mateu, Elvira Romano

Publication date: 3 November 2021

Full work available at URL: https://doaj.org/article/37616da19d6e435f97d5e38a2d409348

zbMATH Keywords

tumor growthprediction methodsgeometric methodsspace-time modeling

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• Approximate Bayesian inference for hierarchical Gaussian Markov random field models
• On the Williams-Bjerknes tumour growth model. I
• Functional data analysis in shape analysis
• A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)
• Bayesian spatio-temporal prediction of cancer dynamics
• Functional data analysis.
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Gaussian Markov Random Fields
• Unnamed Item

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