Maximum-a-posteriori estimation with unknown regularisation parameters

@article{Pereyra2015MaximumaposterioriEW,
  title={Maximum-a-posteriori estimation with unknown regularisation parameters},
  author={Marcelo Pereyra and Josx00E9 M. Bioucas-Dias and M{\'a}rio A. T. Figueiredo},
  journal={2015 23rd European Signal Processing Conference (EUSIPCO)},
  year={2015},
  pages={230-234}
}
This paper presents two hierarchical Bayesian methods for performing maximum-a-posteriori inference when the value of the regularisation parameter is unknown. The methods are useful for models with homogenous regularisers (i.e., prior sufficient statistics), including all norms, composite norms and compositions of norms with linear operators. A key contribution of this paper is to show that for these models the normalisation factor of the prior has a closed-form analytic expression. This then… CONTINUE READING
Highly Cited
This paper has 22 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 17 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 18 references

Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing, ISTE Ltd and John

  • J.-F. Giovannelli, J. Idier
  • 2015
1 Excerpt

Estimating the granularity parameter of a Potts-Markov random field within an MCMC algorithm

  • M. Pereyra, N. Dobigeon, H. Batatia, J.-Y. Tourneret
  • IEEE Trans. Image Process., vol. 22, no. 6, pp…
  • 2013
1 Excerpt

Convex Analysis and Monotone Operator Theory in Hilbert Spaces

  • H. H. Bauschke, P. L. Combettes
  • 2011
1 Excerpt

Similar Papers

Loading similar papers…