Expectation-Propagation algorithms for linear regression with Poisson noise: application to photon-limited spectral unmixing

Yoann Altmann, Alessandro Perelli, Mike E. Davies

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

1 Citation (Scopus)
14 Downloads (Pure)

Abstract

This paper discusses Expectation-Propagation (EP) methods for approximate Bayesian inference in the context of linear regression with Poisson noise. We review two main factor graphs used for generalized linear models and discuss how different EP algorithms can be derived. The estimation per- formance based on EP approximations is compared to the per- formance using Monte Carlo sampling from the exact poste- rior distribution. In particular, we observe that using locally independent or isotropic approximate factors enables more robust and scalable algorithms while providing reliable pos- terior means and marginal variances.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PublisherIEEE
Pages5067-5071
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - 17 Apr 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing 2019
Abbreviated titleICASSP 2019
CountryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • Approximate Bayesian inference
  • Expectation-Propagation
  • Poisson noise
  • linear regression

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Altmann, Y., Perelli, A., & Davies, M. E. (2019). Expectation-Propagation algorithms for linear regression with Poisson noise: application to photon-limited spectral unmixing. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 5067-5071). [8682479] IEEE. https://doi.org/10.1109/ICASSP.2019.8682479