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Books > Computing & IT > General theory of computing
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The Variational Bayes Method in Signal Processing (Paperback, Softcover reprint of hardcover 1st ed. 2006)
Loot Price: R2,927
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The Variational Bayes Method in Signal Processing (Paperback, Softcover reprint of hardcover 1st ed. 2006)
Series: Signals and Communication Technology
Expected to ship within 10 - 15 working days
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Gaussian linear modelling cannot address current signal processing
demands. In moderncontexts,
suchasIndependentComponentAnalysis(ICA), progresshasbeen made
speci?cally by imposing non-Gaussian and/or non-linear assumptions.
Hence, standard Wiener and Kalman theories no longer enjoy their
traditional hegemony in the ?eld, 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 pre- cupations 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 - sign 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 ex- ple, are standard approaches to handling dif?cult 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 -
countered. Since the 1990s, stochastic simulation methods have come
to dominate Bayesian signal processing. Markov Chain Monte Carlo
(MCMC) sampling, and - lated methods, are appreciated for their
ability to simulate possibly high-dimensional distributions to
arbitrary levels of accuracy. More recently, the particle ?ltering
- proach has addressed on-line stochastic simulation. Nevertheless,
the wider acce- ability of these methods-and, to some extent,
Bayesian signal processing itself- has been undermined by the large
computational demands they typically mak
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