Basics of Bayesian Learning - Basically Bayes

Jan Larsen
Informatics and Mathematical Modelling, Technical University of Denmark.
http://www.imm.dtu.dk/~jl/

Jan Larsen received the M.Sc. and Ph.D. degrees in electrical engineering from the Technical University of Denmark (DTU) in 1989 and 1994. Dr. Larsen is currently Associate Professor of Digital Signal Processing at Informatics and Mathematical Modelling, DTU. Jan Larsen has authored and co-authored more than 90 papers and book chapters within the areas of nonlinear statistical signal processing, machine learning, neural networks and datamining with applications to biomedicine, monitoring systems, multimedia, and webmining. He has participated in several national and international research programs, and has served as reviewer for many international journals, conferences, publishing companies and research funding organizations. Further he took part in conference organizations, among others, the IEEE Workshop on Machine Learning for Signal Processing (formerly Neural Networks for Signal Processing) 1999-2006. Currently he is chair of the IEEE Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing Society, and chair of IEEE Denmark Section's Signal Processing Chapter. He is a senior member of The Institute of Electrical and Electronics Engineers. Editorial Board Member of Signal Processing, Elsevier, 2006-2009, and guest editorships involves IEEE Transactions on Neural Networks; Journal of VLSI Signal Processing Systems; and Neurocomputing.

Abstract
The tutorial focuses on the basic elements of Bayesian learning and its relation to classical learning paradigms. This includes a critical discussion of the pros and cons. The theory is illustrated by specific models and examples.

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