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Communication Dans Un Congrès Année : 2011

A gradient-like variational Bayesian algorithm

Aurélia Fraysse
Thomas Rodet

Résumé

In this paper we provide a new algorithm allowing to solve a variational Bayesian issue which can be seen as a functional optimization problem. The main contribution of this paper is to transpose a classical iterative algorithm of optimization in the metric space of probability densities involved in the Bayesian methodology. Another important part is the application of our algorithm to a class of linear inverse problems where estimated quantities are assumed to be sparse. Finally, we compare performances of our method with classical ones on a tomographic problem. Preliminary results on a small dimensional example show that our new algorithm is faster than the classical approaches for the same quality of reconstruction.
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Dates et versions

hal-00611193 , version 1 (20-04-2012)

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  • HAL Id : hal-00611193 , version 1

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Aurélia Fraysse, Thomas Rodet. A gradient-like variational Bayesian algorithm. Stastistical Signal Processing Workshop, Jun 2011, Nice, France. pp.605. ⟨hal-00611193⟩
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