Abstract
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 language | English |
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Title of host publication | 2016 24th European Signal Processing Conference (EUSIPCO) |
Publisher | IEEE |
Pages | 528-532 |
Number of pages | 5 |
ISBN (Electronic) | 9780992862657 |
DOIs | |
Publication status | Published - 1 Dec 2016 |
Event | 24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary Duration: 29 Aug 2016 → 2 Sept 2016 Conference number: 24 |
Publication series
Name | European Signal Processing Conference (EUSIPCO) |
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Publisher | IEEE |
ISSN (Print) | 2076-1465 |
Conference
Conference | 24th European Signal Processing Conference 2016 |
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Abbreviated title | EUSIPCO 2016 |
Country/Territory | Hungary |
City | Budapest |
Period | 29/08/16 → 2/09/16 |
Keywords
- Bayesian inference
- Computational imaging
- Markov chain Monte Carlo
- Model selection
- Statistical signal processing
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering