New Loss Functions in Bayesian Imaging
作者:
Hävard Rue,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 431
页码: 900-908
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476589
出版商: Taylor & Francis Group
关键词: Bayesian inference;Image reconstruction;Image restoration;Markov random field
数据来源: Taylor
摘要:
Unlike the development of more accurate prior distributions for use in Bayesian imaging, the design of more sensible estimators through loss functions has been neglected in the literature. We discuss the design of loss functions with a local structure that depend only on a binary misclassification vector. The proposed approach is similar to modeling with a Markov random field. The Bayes estimate is calculated in a two-step algorithm using Markov chain Monte Carlo and simulated annealing algorithms. We present simulation experiments with the Ising model, where the observations are corrupted with Gaussian and flip noise.
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