Scale Mixture Modeling of Priors for Sparse Signal Recovery
|Event Date:||April 18, 2019|
|Speaker Affiliation:||University of California, San Diego|
|School or Program:||Electrical and Computer Engineering
University of California, San Diego
This talk will discuss Bayesian approaches to solving the sparse signal recovery problem. In particular, methods based on priors that admit a scale mixture representation will be discussed with emphasis on Gaussian scale mixture modeling. In the context of MAP estimation, iterative reweighted approaches will be developed. The scale mixture modeling naturally leads a hierarchical framework and empirical Bayesian methods motivated by this hierarchy will be highlighted. The pros and cons of the two approaches, MAP versus Empirical Bayes, will be a subject of discussion.
Bhaskar D. Rao received the B.Tech. degree in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, India, in 1979 and the M.S. and Ph.D. degrees from the University of Southern California, Los Angeles, in 1981 and 1983, respectively. Since 1983, he has been with the University of California at San Diego, La Jolla, where he is currently a Distinguished Professor in the Electrical and Computer Engineering department . He is the holder of the Ericsson endowed chair in Wireless Access Networks and was the Director of the Center for Wireless Communications (2008-2011). Prof. Rao’s interests are in the areas of digital signal processing, estimation theory, and optimization theory, with applications to digital communications, speech signal processing, and biomedical signal processing.
Host: Assistant Professor Stanley Chan, email@example.com, 49-60230