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[电子工程] The Variational Bayes Method in Signal Processing

The Variational Bayes Method in Signal Processing

The Variational Bayes Method in Signal Processing

book 收藏 
 作者 V´aclav ˇSm´ıdl Anthony Quinn
 ISBN ISBN-10 3-540-28819-
 出版日期/版本 2006/ Springer
 格式 PDF Adobe Acrobat

主题内容

Gaussian linear modelling cannot address current signal processing demands. In modern contexts, such as Independent Component Analysis (ICA), progress has been made specifically by imposing non-Gaussian and/or non-linear assumptions. Hence, standard Wiener and Kalman theories no longer enjoy their traditional hegemony in the field, revealing the standard computational engines for these problems. In their place, diverse principles have been explored, leading to a consequent diversity in the implied computational algorithms. The traditional on-line and data-intensive preoccupations of signal processing continue to demand that these algorithms be tractable. Increasingly, full probability modelling (the so-called Bayesian approach)—or partial probability modelling using the likelihood function—is the pathway for design of these algorithms. However, the results are often intractable, and so the area of distributional approximation is of increasing relevance in signal processing. The Expectation-Maximization (EM) algorithm and Laplace approximation, for example, are standard approaches to handling difficult models, but these approximations (certainty equivalence, and Gaussian, respectively) are often too drastic to handle the high-dimensional, multi-modal and/or strongly correlated problems that are encountered. Since the 1990s, stochastic simulation methods have come to dominate Bayesian signal processing. Markov Chain Monte Carlo (MCMC) sampling, and related methods, are appreciated for their ability to simulate possibly high-dimensional distributions to arbitrary levels of accuracy. More recently, the particle filtering approach has addressed on-line stochastic simulation. Nevertheless, the wider acceptability of these methods—and, to some extent, Bayesian signal processing itself— has been undermined by the large computational demands they typically make..................
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