Bayesian signal processing: classical, modern, and particle by James V. Candy PDF

By James V. Candy

ISBN-10: 0470180943

ISBN-13: 9780470180945

ISBN-10: 0470430575

ISBN-13: 9780470430576

ISBN-10: 0470430583

ISBN-13: 9780470430583

ISBN-10: 1118210549

ISBN-13: 9781118210543

New Bayesian process is helping you remedy difficult difficulties in sign processing very easily. sign processing is predicated in this primary conceptthe extraction of serious info from noisy, doubtful information. so much suggestions depend upon underlying Gaussian assumptions for an answer, yet what occurs while those assumptions are inaccurate? Bayesian suggestions stay clear of this problem by means of supplying a totally different Read more...


This ebook takes the reader from the classical tools of model-based sign processing, to the following iteration of processors that would truly dominate the way forward for model-based sign processing for Read more...

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Extra info for Bayesian signal processing: classical, modern, and particle filtering methods

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ML estimates are consistent. 2. ML estimates are asymptotically efficient with RX|X =  −1 . 3 BATCH MAXIMUM LIKELIHOOD ESTIMATION 25 3. ML estimates are asymptotically Gaussian with  (X, RX̃ X̃ ). 4. ML estimates are invariant, that is, if X̂ ML , then any function of the ML estimate is the ML estimate of the function, f̂ML = f (X̂ ML ). 5. ML estimates of the sufficient statistic are equivalent to the ML estimates over the original data. These properties are asymptotic and therefore imply that a large amount of data must be available for processing.

To be more precise, we mathematically formulate the general “missing data” problem by first defining the unknown parameters or variables to be estimated as ???? ∈ N???? ×1 with ???? ∈ Θ, the parameter space. We further define three distinct spaces for our problem: (1) the complete data space, ; (2) the incomplete data space, ; and (3) the missing data space, , where the complete space is the union:  = (, ). Analogously, we define the corresponding complete, incomplete, and missing/hidden vectors as: z ∈ Nz ×1 , y ∈ Ny ×1 , and x ∈ Nx ×1 , respectively.

Casella, Monte Carlo Statistical Methods (New York: Springer, 1999). 16 INTRODUCTION 13. M. Tanner, Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 2nd Ed. (New York: Springer-Verlag, 1993). 14. J. Ruanaidh and W. Fitzgerald, Numerical Bayesian Methods Applied to Signal Processing (New York: Springer-Verlag, 1996). 15. W. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice (New York: Chapman & Hall/CRC Press, 1996).

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Bayesian signal processing: classical, modern, and particle filtering methods by James V. Candy

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