Parametric Bilinear Generalized Approximate Message Passing - implementation -

Today, we have an extension of BiG-AMP, woohoo !

 

From ITA15 slide: Parametric Bilinear Generalized Approximate Message Passing by Phil Schniter and Jason Parker

Parametric Bilinear Generalized Approximate Message Passing by Jason T. Parker, Yan Shou, Philip Schniter

We propose a scheme to estimate the parameters bi and cj of the bilinear form zm=∑i,jbiz(i,j)mcj from noisy measurements {ym}Mm=1, where ym and zm are related through an arbitrary likelihood function and z(i,j)m are known. Our scheme is based on generalized approximate message passing (G-AMP): it treats bi and cj as random variables and z(i,j)m as an i.i.d.\ Gaussian tensor in order to derive a tractable simplification of the sum-product algorithm in the large-system limit. It generalizes previous instances of bilinear G-AMP, such as those that estimate matrices B and C from a noisy measurement of Z=BC, allowing the application of AMP methods to problems such as self-calibration, blind deconvolution, and matrix compressive sensing. Numerical experiments confirm the accuracy and computational efficiency of the proposed approach.

The implementation can be found here: http://gampmatlab.wikia.com/wiki/Generalized_Approximate_Message_Passing 

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