Comparing Bayesian models in the absence of ground truth

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

2 Citations (Scopus)


Modern signal processing methods rely strongly on Bayesian statistical models to solve challenging problems. This paper considers the objective comparison of two alternative Bayesian models, for scenarios with no ground truth available, and with a focus on model selection. Existing model selection approaches are generally difficult to apply to signal processing because they are unsuitable for models with priors that are improper or vaguely informative, and because of challenges related to high dimensionality. This paper presents a general methodology to perform model selection for models that are high-dimensional and that involve proper, improper, or vague priors. The approach is based on an additive mixture meta-model representation that encompasses both models and which concentrates on the model that fits the data best, and relies on proximal Markov chain Monte Carlo algorithms to perform high-dimensional computations efficiently. The methodology is demonstrated on a series of experiments related to image resolution enhancement with a total-variation prior.

Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)9780992862657
Publication statusPublished - 1 Dec 2016
Event24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary
Duration: 29 Aug 20162 Sept 2016
Conference number: 24

Publication series

NameEuropean Signal Processing Conference (EUSIPCO)
ISSN (Print)2076-1465


Conference24th European Signal Processing Conference 2016
Abbreviated titleEUSIPCO 2016


  • Bayesian inference
  • Computational imaging
  • Markov chain Monte Carlo
  • Model selection
  • Statistical signal processing

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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