Robust Bayesian synthetic likelihood via a semi-parametric approach-Reference-Cited by-同舟云学术

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Robust Bayesian synthetic likelihood via a semi-parametric approach

Published:2019-10-04 Issue:3 Volume:30 Page:543-557
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

An Ziwen^ORCID,Nott David J.,Drovandi Christopher^ORCID

Funder

Australian Research Council’s Discovery Early Career Researcher Award

Singapore Ministry of Education Academic Research Fund Tier 1 grant

Publisher

Springer Science and Business Media LLC

Subject

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

Link

http://link.springer.com/content/pdf/10.1007/s11222-019-09904-x.pdf

Reference36 articles.

1. An, Z., South, L.F., Nott, D.J., Drovandi, C.C.: Accelerating Bayesian synthetic likelihood with the graphical lasso. J. Comput. Graph. Stat. 28(2), 471–475 (2019). https://doi.org/10.1080/10618600.2018.1537928

2. Anderson, C., Coles, S.: The largest inclusions in a piece of steel. Extremes 5(3), 237–252 (2002)

3. Andrieu, C., Roberts, G.O.: The pseudo-marginal approach for efficient Monte Carlo computations. Ann. Stat. 37(2), 697–725 (2009)

4. Barbu, C.M., Sethuraman, K., Billig, E.M.W., Levy, M.Z.: Two-scale dispersal estimation for biological invasions via synthetic likelihood. Ecography 41(4), 661–672 (2017)

5. Bedford, T., Cooke, R.M.: Vines-a new graphical model for dependent random variables. Ann. Stat. 30(4), 1031–1068 (2002)

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