A gradient-like variational Bayesian algorithm

Aurélia Fraysse 1 Thomas Rodet 1
1 Division Signaux - L2S
L2S - Laboratoire des signaux et systèmes : 1289
Abstract : 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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Submitted on : Friday, April 20, 2012 - 12:01:15 PM
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  • HAL Id : hal-00611193, version 1



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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