A Variational Bayes Approach to Blind Channel Estimation

A Variational Bayes Approach to Robust Principal Component Analysis

We solve the Robust Principal Component Analysis problem: decomposing an observed matrix into a low-rank matrix plus a sparse matrix. Unlike alternative methods that approximate this l0 objective with an l1 objective and solve a convex optimization problem, we develop a corresponding generative model and solve a statistical inference problem. The main advantages of this approach is its ability ...

Variational Bayes Estimation of Mixing Coefficients

We investigate theoretically some properties of variational Bayes approximations based on estimating the mixing coefficients of known densities. We show that, with probability 1 as the sample size n grows large, the iterative algorithm for the variational Bayes approximation converges locally to the maximum likelihood estimator at the rate of O(1/n). Moreover, the variational posterior distribu...

A Variational Approach to Nonlinear Estimation

We consider estimation problems, in which the estimand, X, and observation, Y , take values in measurable spaces. Regular conditional versions of the forward and inverse Bayes formula are shown to have dual variational characterisations involving the minimisation of an apparent information, and the maximisation of a compatible information. These both have natural information theoretic interpret...

Blind Gsm Channel Estimation

The bandwidth efficiency of many comm. systems could be improved if the transmission channel was estimated blindly, i.e. without resort to training sequences. As an example, we investigate in this paper the applicability of two algorithms for the blind identification of mixed-phase linear time-invariant FIR systems to the estimation of mobile radio channels on GSM conditions. Both approaches ex...

The Variational Bayes Approach in Signal Processing