By Mark Girolami
It covers themes similar to using hidden Markov tools, the independence assumption, and topographic ICA, and contains instructional chapters on Bayesian and variational techniques. It additionally presents the most recent techniques to ICA difficulties, together with an research into yes "hard difficulties" for the first actual time.
Comprising contributions from the main revered and leading edge researchers within the box, this quantity could be of curiosity to scholars and researchers in desktop technology and electric engineering; learn and improvement team of workers in disciplines equivalent to statistical modelling and information research; bio-informatic staff; and physicists and chemists requiring novel info research methods.
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Extra info for Advances in Independent Component Analysis (Perspectives in Neural Computing)
Advances in Independent Component Analysis (Perspectives in Neural Computing) by Mark Girolami