This release completes the replication of certain details from the following paper by Jeremy Vila and Philip Schniter: J. P. Vila and P. Schniter, "Expectation-Maximization Bernoulli-Gaussian Approximate Message Passing," Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2011
We consider a class of approximated message passing (AMP) algorithms and characterize their high-dim...
We discuss the expectation propagation (EP) algorithm for approximate Bayesian inference using a fac...
Expectation propagation (EP) is a widely successful algorithm for variational inference. EP is an it...
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become extremely popul...
We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projec...
We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood lea...
Abstract—We consider the estimation of an i.i.d. random vector observed through a linear transform f...
Gaussian belief propagation (BP) is known to be an efficient message-passing algorithm for calculati...
Contains fulltext : 32541.pdf (author's version ) (Open Access
We consider the problem of reconstructing the signal and the hidden variables from observations comi...
This is the final version of the article. It first appeared from Neural Information Processing Syste...
Gaussian belief propagation (GaBP) is an iterative algorithm for computing the mean (and variances) ...
Contains fulltext : 62669.pdf (author's version ) (Open Access
This paper investigates a large unitarily invariant system (LUIS) involving a unitarily invariant se...
We design iterative receiver schemes for a generic communication system by treating channel estimati...
We consider a class of approximated message passing (AMP) algorithms and characterize their high-dim...
We discuss the expectation propagation (EP) algorithm for approximate Bayesian inference using a fac...
Expectation propagation (EP) is a widely successful algorithm for variational inference. EP is an it...
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become extremely popul...
We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projec...
We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood lea...
Abstract—We consider the estimation of an i.i.d. random vector observed through a linear transform f...
Gaussian belief propagation (BP) is known to be an efficient message-passing algorithm for calculati...
Contains fulltext : 32541.pdf (author's version ) (Open Access
We consider the problem of reconstructing the signal and the hidden variables from observations comi...
This is the final version of the article. It first appeared from Neural Information Processing Syste...
Gaussian belief propagation (GaBP) is an iterative algorithm for computing the mean (and variances) ...
Contains fulltext : 62669.pdf (author's version ) (Open Access
This paper investigates a large unitarily invariant system (LUIS) involving a unitarily invariant se...
We design iterative receiver schemes for a generic communication system by treating channel estimati...
We consider a class of approximated message passing (AMP) algorithms and characterize their high-dim...
We discuss the expectation propagation (EP) algorithm for approximate Bayesian inference using a fac...
Expectation propagation (EP) is a widely successful algorithm for variational inference. EP is an it...