On Misspecified Parameter Bounds with Application to Sparse Bayesian Learning

Christ D. Richmond, Abdulhakim Alhowaish

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The sparse vector recovery problem can lead to a combinatorial search of prohibitive computations. Hence, reformulations amenable to convex optimization strategies have been considered. Alternatively, Bayesian inference approaches can curtail computations such as variational Bayesian methods (VBM). VBM, however, intentionally introduces a misspecified model to improve the efficiency of computational requirements. This talk will review the theory of misspecified parameter bounds and extensions to the Bayesian framework. Additionally, it will be shown that misspecified bounds can provide tight prediction of sparse Bayesian learning approaches, and thus can be used to tune the hyperparameters of VBM for improved performance. The VBM gains in computational efficiency, however, come at the cost of increased mean squared error (MSE) when compared to the perfectly specified model case. Examples will be shown that quantify this MSE increase and illustrate the apparent tradespace.

Original languageEnglish (US)
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1472-1476
Number of pages5
ISBN (Electronic)9780738131269
DOIs
StatePublished - Nov 1 2020
Externally publishedYes
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: Nov 1 2020Nov 5 2020

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2020-November
ISSN (Print)1058-6393

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period11/1/2011/5/20

Keywords

  • Bayesian
  • Cramér-Rao bound
  • learning
  • misspecified
  • parameter bound
  • sparse

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'On Misspecified Parameter Bounds with Application to Sparse Bayesian Learning'. Together they form a unique fingerprint.

Cite this