| dc.creator |
Chen, X |
|
| dc.creator |
Hobson, M |
|
| dc.creator |
Das, S |
|
| dc.creator |
Gelderblom, P |
|
| dc.date |
2018-11-21T12:05:48Z |
|
| dc.date |
2019-11-19 |
|
| dc.date |
2018-11-21T12:05:48Z |
|
| dc.date.accessioned |
2022-05-27T01:02:15Z |
|
| dc.date.available |
2022-05-27T01:02:15Z |
|
| dc.identifier |
Published online 19 November 2018 |
|
| dc.identifier |
10.1007/s11222-018-9841-3 |
|
| dc.identifier |
http://hdl.handle.net/10871/34839 |
|
| dc.identifier |
1573-1375 |
|
| dc.identifier |
Statistics and Computing |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/241882 |
|
| dc.description |
This is the final version. Available on open access from Springer via the DOI in this record |
|
| dc.description |
In real-world Bayesian inference applications, prior assumptions regarding the parameters of interest may be unrepresentative of their actual values for a given dataset. In particular, if the likelihood is concentrated far out in the wings of the assumed prior distribution, this can lead to extremely inefficient exploration of the resulting posterior by nested sampling (NS) algorithms, with unnecessarily high associated computational costs. Simple solutions such as broadening the prior range in such cases might not be appropriate or possible in real-world applications, for example when one wishes to assume a single standardised prior across the analysis of a large number of datasets for which the true values of the parameters of interest may vary. This work therefore introduces a posterior repartitioning (PR) method for NS algorithms, which addresses the problem by redefining the likelihood and prior while keeping their product fixed, so that the posterior inferences and evidence estimates remain unchanged but the efficiency of the NS process is significantly increased. Numerical results show that the PR method provides a simple yet powerful refinement for NS algorithms to address the issue of unrepresentative priors. |
|
| dc.language |
en |
|
| dc.publisher |
Springer |
|
| dc.rights |
© The Author(s) 2018. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
|
| dc.subject |
Bayesian modelling |
|
| dc.subject |
Nested sampling |
|
| dc.subject |
Unrepresentative prior |
|
| dc.subject |
Posterior repartitioning |
|
| dc.title |
Improving the efficiency and robustness of nested sampling using posterior repartitioning |
|
| dc.type |
Article |
|