The Library
Likelihood-free inference by ratio estimation
Tools
Tools
Tools
Thomas, Owen, Dutta, Ritabrata, Corander, Jukka, Kaski, Samuel and Gutmann, Michael U. (2022) Likelihood-free inference by ratio estimation. Bayesian Analysis, 17 (1). pp. 1-31. doi:10.1214/20-BA1238 ISSN 1931-6690.
|
PDF
WRAP-Likelihood-free-inference-by-ratio-estimation-Dutta-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (703Kb) | Preview |
Official URL: http://dx.doi.org/10.1214/20-BA1238
Abstract
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference in the absence of a likelihood function. The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution. In another popular approach called approximate Bayesian computation, the inference is performed by identifying parameter values for which the summary statistics of the simulated data are close to those of the observed data. Synthetic likelihood is easier to use as no measure of “closeness” is required but the Gaussianity assumption is often limiting. Moreover, both approaches require judiciously chosen summary statistics. We here present an alternative inference approach that is as easy to use as synthetic likelihood but not as restricted in its assumptions, and that, in a natural way, enables automatic selection of relevant summary statistic from a large set of candidates. The basic idea is to frame the problem of estimating the posterior as a problem of estimating the ratio between the data generating distribution and the marginal distribution. This problem can be solved by logistic regression, and including regularising penalty terms enables automatic selection of the summary statistics relevant to the inference task. We illustrate the general theory on canonical examples and employ it to perform inference for challenging stochastic nonlinear dynamical systems and high-dimensional summary statistics.
| Item Type: | Journal Article | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | H Social Sciences > HA Statistics Q Science > QA Mathematics |
|||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||
| Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Mathematical analysis, Estimation theory, Logistic regression analysis , Stochastic systems | |||||||||
| Journal or Publication Title: | Bayesian Analysis | |||||||||
| Publisher: | International Society for Bayesian Analysis | |||||||||
| ISSN: | 1931-6690 | |||||||||
| Official Date: | 2022 | |||||||||
| Dates: |
|
|||||||||
| Volume: | 17 | |||||||||
| Number: | 1 | |||||||||
| Page Range: | pp. 1-31 | |||||||||
| DOI: | 10.1214/20-BA1238 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 1 April 2022 | |||||||||
| Date of first compliant Open Access: | 1 April 2022 | |||||||||
| RIOXX Funder/Project Grant: |
|
|||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
