Unifying message passing algorithms under the framework of constrained Bethe free energy minimization
Variational message passing (VMP), belief propagation (BP) and expectation propagation
(EP) have found their wide applications in complex statistical signal processing problems. In …
(EP) have found their wide applications in complex statistical signal processing problems. In …
Fast fading channel estimation by Kalman filtering and CIR support tracking
Z Jellali, LN Atallah - IEEE Transactions on Broadcasting, 2017 - ieeexplore.ieee.org
Structured estimation of channel impulse response (CIR) is considered in orthogonal
frequency division multiplexing (OFDM) systems for which the channel exhibits a sparse …
frequency division multiplexing (OFDM) systems for which the channel exhibits a sparse …
Unifying message passing algorithms under the framework of constrained bethe free energy minimization
Variational message passing (VMP), belief propagation (BP) and expectation propagation
(EP) have found their wide applications in complex statistical signal processing problems. In …
(EP) have found their wide applications in complex statistical signal processing problems. In …
Nearly consistent finite particle estimates in streaming importance sampling
In Bayesian inference, we seek to compute information about random variables such as
moments or quantiles on the basis of available data and prior information. When the …
moments or quantiles on the basis of available data and prior information. When the …
Variational Sparse Bayesian Learning for Estimation of Gaussian Mixture Distributed Wireless Channels
L Kong, X Zhang, H Zhao, J Wei - Entropy, 2021 - mdpi.com
In this paper, variational sparse Bayesian learning is utilized to estimate the multipath
parameters for wireless channels. Due to its flexibility to fit any probability density function …
parameters for wireless channels. Due to its flexibility to fit any probability density function …
[PDF][PDF] Approximate shannon sampling in importance sampling: Nearly consistent finite particle estimates
In Bayesian inference, we seek to compute information about random variables such as
moments or quantiles on the basis of data and prior information. When the distribution of …
moments or quantiles on the basis of data and prior information. When the distribution of …
Compressed streaming importance sampling for efficient representations of localization distributions
Importance sampling (IS) is the standard Monte Carlo tool to compute integrals involving
random variables such as their mean or higher-order moments. This procedure permits …
random variables such as their mean or higher-order moments. This procedure permits …