File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Scopus: eid_2-s2.0-85084009194
- WOS: WOS:000535866907052
- Find via
Supplementary
- Citations:
- Appears in Collections:
Conference Paper: Optimistic distributionally robust optimization for nonparametric likelihood approximation
Title | Optimistic distributionally robust optimization for nonparametric likelihood approximation |
---|---|
Authors | |
Issue Date | 2019 |
Citation | Advances in Neural Information Processing Systems, 2019, v. 32 How to Cite? |
Abstract | The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood that identifies a probability measure which lies in the neighborhood of the nominal measure and that maximizes the probability of observing the given sample point. We show that when the neighborhood is constructed by the Kullback-Leibler divergence, by moment conditions or by the Wasserstein distance, then our optimistic likelihood can be determined through the solution of a convex optimization problem, and it admits an analytical expression in particular cases. We also show that the posterior inference problem with our optimistic likelihood approximation enjoys strong theoretical performance guarantees, and it performs competitively in a probabilistic classification task. |
Persistent Identifier | http://hdl.handle.net/10722/313627 |
ISSN | 2020 SCImago Journal Rankings: 1.399 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nguyen, Viet Anh | - |
dc.contributor.author | Shafieezadeh-Abadeh, Soroosh | - |
dc.contributor.author | Yue, Man Chung | - |
dc.contributor.author | Kuhn, Daniel | - |
dc.contributor.author | Wiesemann, Wolfram | - |
dc.date.accessioned | 2022-06-23T01:18:47Z | - |
dc.date.available | 2022-06-23T01:18:47Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Advances in Neural Information Processing Systems, 2019, v. 32 | - |
dc.identifier.issn | 1049-5258 | - |
dc.identifier.uri | http://hdl.handle.net/10722/313627 | - |
dc.description.abstract | The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood that identifies a probability measure which lies in the neighborhood of the nominal measure and that maximizes the probability of observing the given sample point. We show that when the neighborhood is constructed by the Kullback-Leibler divergence, by moment conditions or by the Wasserstein distance, then our optimistic likelihood can be determined through the solution of a convex optimization problem, and it admits an analytical expression in particular cases. We also show that the posterior inference problem with our optimistic likelihood approximation enjoys strong theoretical performance guarantees, and it performs competitively in a probabilistic classification task. | - |
dc.language | eng | - |
dc.relation.ispartof | Advances in Neural Information Processing Systems | - |
dc.title | Optimistic distributionally robust optimization for nonparametric likelihood approximation | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-85084009194 | - |
dc.identifier.volume | 32 | - |
dc.identifier.isi | WOS:000535866907052 | - |