Journal article
Model misspecification in approximate Bayesian computation: consequences and diagnostics
- Abstract:
-
We analyse the behaviour of approximate Bayesian computation (ABC) when the model generating the simulated data differs from the actual data‐generating process, i.e. when the data simulator in ABC is misspecified. We demonstrate both theoretically and in simple, but practically relevant, examples that when the model is misspecified different versions of ABC can yield substantially different results. Our theoretical results demonstrate that even though the model is misspecified, under regulari...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Bibliographic Details
- Publisher:
- Wiley Publisher's website
- Journal:
- Journal of the Royal Statistical Society: Series B Journal website
- Volume:
- 82
- Issue:
- 2
- Pages:
- 421-444
- Publication date:
- 2020-01-08
- DOI:
- EISSN:
-
1467-9868
- ISSN:
-
1369-7412
Item Description
- Language:
- English
- Pubs id:
-
pubs:1081370
- UUID:
-
uuid:1b5a5f77-60c4-4b46-837e-cdc0d3faea44
- Local pid:
- pubs:1081370
- Source identifiers:
-
1081370
- Deposit date:
- 2020-01-09
Terms of use
- Copyright holder:
- Royal Statistical Society
- Copyright date:
- 2020
- Rights statement:
- © 2020 Royal Statistical Society
- Notes:
- This is the accepted manuscript version of the article. The final version is available from Wiley at: https://doi.org/10.1111/rssb.12356
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