Results 31 to 40 of about 1,281,025 (295)
Variance state propagation for uplink channel estimation in LEO satellite systems
Electronics Letters, 2023 This paper, considers the channel estimation problem of the uplink low Earth orbit (LEO) satellite orthogonal frequency division multiplexing (OFDM) system.
Xiaoyong Lei +3 more
doaj +1 more source
Stochastic Expectation Propagation
CoRR, 2015 Expectation propagation (EP) is a deterministic approximation algorithm that is often used to perform approximate Bayesian parameter learning. EP approximates the full intractable posterior distribution through a set of local approximations that are iteratively refined for each datapoint.
Li, Yingzhen +2 more
openaire +4 more sources
Patch-Based Image Restoration using Expectation Propagation [PDF]
SIAM Journal of Imaging Sciences, 2021 This paper presents a new Expectation Propagation (EP) framework for image restoration using patch-based prior distributions. While Monte Carlo techniques are classically used to sample from intractable posterior distributions, they can suffer from ...
Dan Yao, S. Mclaughlin, Y. Altmann
semanticscholar +1 more source
Expectation Propagation in the Large Data Limit [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2017 SummaryExpectation propagation (EP) is a widely successful algorithm for variational inference. EP is an iterative algorithm used to approximate complicated distributions, typically to find a Gaussian approximation of posterior distributions. In many applications of this type, EP performs extremely well.
Dehaene, Guillaume, Barthelme, Simon
openaire +5 more sources
GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection [PDF]
Global Communications ConferenceMassive MIMO (multiple-input multiple-output) detection is an important topic in wireless communication and various machine learning based methods have been developed recently for this task.
Qincheng Lu, Sitao Luan, Xiaoming Chang
semanticscholar +1 more source
Fast Scalable Image Restoration Using Total Variation Priors and Expectation Propagation [PDF]
IEEE Transactions on Image Processing, 2021 This paper presents a scalable approximate Bayesian method for image restoration using Total Variation (TV) priors, with the ability to offer uncertainty quantification.
D. Yao, S. Mclaughlin, Y. Altmann
semanticscholar +1 more source
Bilinear Adaptive Generalized Vector Approximate Message Passing
IEEE Access, 2019 This paper considers the generalized bilinear recovery problem, which aims to jointly recover the vector b and the matrix X from componentwise nonlinear measurements ${\text {Y}}\sim p({\text {Y}}|{\text {Z}})=\prod \limits _{i,j}p(Y_{ij}|Z_{ij ...
Xiangming Meng, Jiang Zhu
doaj +1 more source
Low Complexity Receiver via Expectation Propagation for OTFS Modulation
IEEE Communications Letters, 2021 Orthogonal time frequency space (OTFS) modulation has recently received considerable attention due to its capabilities to exploit full time-frequency diversity and deal with channel dynamics in high-mobility scenarios. However, the implementation of high-
Hua Li +5 more
semanticscholar +1 more source
Improving Cell-Free Massive MIMO Detection Performance via Expectation Propagation [PDF]
IEEE Vehicular Technology Conference, 2021 Cell-free (CF) massive multiple-input multiple-output (M-MIMO) technology plays a prominent role in the beyond fifth-generation (5G) networks. However, designing a high performance CF M-MIMO detector is a challenging task due to the presence of pilot ...
Alva Kosasih +4 more
semanticscholar +1 more source
A Study of Using Bethe/Kikuchi Approximation for Learning Directed Graphic Models
IEEE Access, 2021 This paper applies the variational methods to learn the parameters and the probability of evidence of directed graphic models (also known as Bayesian networks (BNs)) when data contains missing values.
Peng Lin, Martin Neil, Norman Fenton
doaj +1 more source