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