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Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning
Asynchronous impulsive noise cyclostationary noise PLC OFDM sparse Bayesian learning
2013/4/27
Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additive white Gaussian noi...
Bayesian learning of joint distributions of objects
Bayesian learning joint distributions objects
2013/4/27
There is increasing interest in broad application areas in defining flexible joint models for data having a variety of measurement scales, while also allowing data of complex types, such as functions,...
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Bayesian Learning Temporally Correlated Signal Recovery
2011/3/23
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorith...
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Signal Recovery Temporally Correlated Bayesian Learning
2011/3/22
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorith...
Efficient Bayesian Learning in Social Networks with Gaussian Estimators
Efficient Bayesian Learning Social Networks Gaussian Estimators
2010/3/10
We propose a simple and efficient Bayesian model of iterative learning on social networks.
This model is efficient in two senses: the process both results in an optimal belief, and can
be carried ou...
Variational Bayesian Learning of Directed Graphical Models with Hidden Variables
Approximate Bayesian Inference Bayes Factors Directed Acyclic Graphs EM Algorithm Graphical Models Markov Chain Monte Carlo
2009/9/21
A key problem in statistics and machine learning is inferring suitable
structure of a model given some observed data. A Bayesian approach to model
comparison makes use of the marginal likelihood of ...
On Bayesian learning from Bernoulli observations
Asymptotics Kullback–Leibler divergence Loss function
2010/3/18
We provide a reason for Bayesian updating, in the Bernoulli case, even when
it is assumed observations are independent and identically distributed with fixed
but unknown parameter 0. The motivation...