Modularized Bayesian analyses and cutting feedback in likelihood-free inference-Reference-Cited by-同舟云学术

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Modularized Bayesian analyses and cutting feedback in likelihood-free inference

Published:2023-01-19 Issue:1 Volume:33 Page:
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Chakraborty Atlanta,Nott David J.,Drovandi Christopher C.,Frazier David T.,Sisson Scott A.

Funder

Australian Research Council

Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11222-023-10207-5.pdf

Reference57 articles.

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3. Bissiri, P.G., Holmes, C.C., Walker, S.G.: A general framework for updating belief distributions. J. Royal Stat. Soc.: Series B (Stat. Methodol.) 78(5), 1103–1130 (2016)

4. Bonassi, F.V., West, M.: Sequential Monte Carlo with adaptive weights for approximate Bayesian computation. Bayesian Anal. 10(1), 171–187 (2015)

5. Bonassi, F.V., You, L., West, M.: Bayesian learning from marginal data in bionetwork models. Stat. Appl. Genet. Mol. Biol. 10(1), (2011)

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