Inverse problems: From regularization to Bayesian inference

Inverse problems deal with the quest for unknown causes of observed consequences, based on predictive models, known as the forward models, that associate the former quantities to the latter in the causal order. Forward models are usually well‐posed, as causes determine consequences in a unique and s...

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Bibliographic Details
Published in:	Wiley interdisciplinary reviews. Computational statistics Vol. 10; no. 3; pp. e1427 - n/a
Main Authors:	Calvetti, D., Somersalo, E.
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.05.2018 Wiley Subscription Services, Inc
Subjects:	Bayesian analysis Computation Computational mathematics Computer applications Data Data analysis Data processing Frameworks Graphical methods hierarchical Bayesian models Ill‐posedness Inverse problems likelihood Mathematical models Mathematics measurement and modeling errors Modelling Noise measurement Noise sensitivity Numerical methods posterior estimates Prediction models Probability theory Random variables Regularization sparsity‐promoting and sample‐based priors Statistical analysis Statistical inference Statistical methods Statistics structural Tikhonov regularization
ISSN:	1939-5108, 1939-0068
Online Access:	Get full text
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