61 citations · 304 across the 31 of their papers we have counts for
5 papers · 1 filter
Expectation Maximization as Message Passing - Part I: Principles and Gaussian Messages
Justin Dauwels, Andrew Eckford, Sascha Korl +1
It is shown how expectation maximization (EM) may be viewed as a message passing algorithm in factor graphs. In particular, a general EM message computation rule is identified. As…
Belief-Propagation Decoding of Lattices Using Gaussian Mixtures
Brian M. Kurkoski, Justin Dauwels
A belief-propagation decoder for low-density lattice codes is given which represents messages explicitly as a mixture of Gaussians functions. The key component is an algorithm for…
Message-Passing Decoding of Lattices Using Gaussian Mixtures
Brian M. Kurkoski, Justin Dauwels
A lattice decoder which represents messages explicitly as a mixture of Gaussians functions is given. In order to prevent the number of functions in a mixture from growing as the de…
A general computation rule for lossy summaries/messages with examples from equalization
Junli Hu, Hans-Andrea Loeliger, Justin Dauwels +1
Elaborating on prior work by Minka, we formulate a general computation rule for lossy messages. An important special case (with many applications in communications) is the conversi…
Expectation maximization as message passing
J. Dauwels, S. Korl, H. -A. Loeliger
Based on prior work by Eckford, it is shown how expectation maximization (EM) may be viewed, and used, as a message passing algorithm in factor graphs.