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Robustness of Bayesian Analyses

Robustness of Bayesian Analyses

Joseph B. Kadane
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This paper presents algorithms for robustness analysis of Bayesian networks with global neighborhoods. Robust Bayesian inference is the calculation of bounds on posterior valuesgiven perturbations in a probabilistic model. We present algorithms for robust inference (including expected utility, expected value and variance bounds) with global perturbations that can be modeled by ffl-contaminated, constant density ratio, constant density bounded and total variation classes of distributions.
年:
1984
出版社:
North-Holland
语言:
english
页:
326
ISBN 10:
0444862099
ISBN 13:
9780444862099
系列:
Studies in Bayesian econometrics 4
文件:
DJVU, 3.52 MB
IPFS:
CID , CID Blake2b
english, 1984
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