Ergebnisse für: Gaussian Message Passing
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Recovery of signals from distorted or noisy observations has been a longstanding research problem with a wide variety of practical applications. We advocate to approach these types of problems by interpreting them as input estimation in finite-order linear state-space models. Among other application...
The need for analog-to-digital (A/D) and digital-to-analog (D/A) conversion
is a ubiquitous part of many of today’s practical applications. The
research fields of A/D and D/A conversion are multi-disciplinary, involving
topics such as discrete- and continuous-time signal processing, circuit
theory...
Normal with unknown variance (NUV) priors are a central idea of sparse
Bayesian learning and allow variational representations of non-Gaussian
priors. More specifically, such variational representations can be seen
as parameterized Gaussians, wherein the parameters are generally unknown.
The advanta...