Statistics > Computation
arXiv:1809.05800 (stat)
[Submitted on 16 Sep 2018 (v1), last revised 3 Oct 2019 (this version, v2)]
Title:Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach
View a PDF of the paper titled Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach, by Ziwen An and 1 other authors
View PDFAbstract:Bayesian synthetic likelihood (BSL) is now a well established method for performing approximate Bayesian parameter estimation for simulation-based models that do not possess a tractable likelihood function. BSL approximates an intractable likelihood function of a carefully chosen summary statistic at a parameter value with a multivariate normal distribution. The mean and covariance matrix of this normal distribution are estimated from independent simulations of the model. Due to the parametric assumption implicit in BSL, it can be preferred to its non-parametric competitor, approximate Bayesian computation, in certain applications where a high-dimensional summary statistic is of interest. However, despite several successful applications of BSL, its widespread use in scientific fields may be hindered by the strong normality assumption. In this paper, we develop a semi-parametric approach to relax this assumption to an extent and maintain the computational advantages of BSL without any additional tuning. We test our new method, semiBSL, on several challenging examples involving simulated and real data and demonstrate that semiBSL can be significantly more robust than BSL and another approach in the literature.
| Comments: | 37 pages Latex; the paper has been re-organised, moved section 4 and 5 to appendices, moved less important example figures to appendices, added "sensitivity to n" section to appendices, added a shrinkage example to appendices, typos and references corrected |
| Subjects: | Computation (stat.CO); Methodology (stat.ME) |
| Cite as: | arXiv:1809.05800 [stat.CO] |
| (orarXiv:1809.05800v2 [stat.CO] for this version) | |
| https://doi.org/10.48550/arXiv.1809.05800 arXiv-issued DOI via DataCite |
Submission history
From: Ziwen An [view email][v1] Sun, 16 Sep 2018 03:28:16 UTC (1,281 KB)
[v2] Thu, 3 Oct 2019 07:15:12 UTC (5,397 KB)
Full-text links:
Access Paper:
- View PDF
- TeX Source
View a PDF of the paper titled Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach, by Ziwen An and 1 other authors
References & Citations
export BibTeX citationLoading...
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.