Detection of arterial wall abnormalities via Bayesian model selection

Detection of arterial wall abnormalities via Bayesian model selection

Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter...

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Bibliographic Details
Main Authors: Karen Larson, Clark Bowman, Costas Papadimitriou, Petros Koumoutsakos, Anastasios Matzavinos
Format: Article
Language:English
Published: The Royal Society 2019-10-01
Series:Royal Society Open Science
Subjects:
uncertainty quantification
transitional markov chain monte carlo
inverse problem
one-dimensional blood flow
model selection
Online Access:https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.182229

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https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.182229

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