By Mark Girolami
It covers subject matters corresponding to using hidden Markov tools, the independence assumption, and topographic ICA, and contains educational chapters on Bayesian and variational techniques. It additionally offers the newest methods 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 could be of curiosity to scholars and researchers in laptop technology and electric engineering; study and improvement group of workers in disciplines resembling statistical modelling and information research; bio-informatic employees; 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