# New PDF release: Array and Statistical Signal Processing

By Abdelhak M. Zoubir, Mats Viberg, Rama Chellappa and Sergios Theodoridis (Eds.)

ISBN-10: 0124115977

ISBN-13: 9780124115972

This 3rd quantity of a 5 quantity set, edited and authored through global top specialists, supplies a evaluation of the foundations, equipment and strategies of significant and rising study issues and applied sciences in array and statistical sign processing.

With this reference resource you will:

• Quickly take hold of a brand new sector of research
• Understand the underlying ideas of a subject matter and its application
• Ascertain how a subject matter pertains to different parts and examine of the study concerns but to be resolved

• Quick educational reports of significant and rising themes of study in array and statistical sign processing
• Presents middle rules and indicates their application
• Reference content material on middle ideas, applied sciences, algorithms and functions
• Comprehensive references to magazine articles and different literature on which to construct additional, extra particular and unique wisdom
• Edited by way of leading people in the sphere who, via their attractiveness, were capable of fee specialists to jot down on a specific topic

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Additional info for Array and Statistical Signal Processing

Sample text

It may be written as [31] f (y|θˆ ) , ˆ f (Y|θ)dY p∗ (y) = where θˆ denotes the maximum likelihood estimate of θ over the observation set. The denominator is a normalizing factor that is the sum of the maximum likelihoods of all potential observation sets Y acquired in experiments, and the numerator represents the maximized likelihood value. With this solution, the worst case data regret is minimized. It has been further shown that by solving a related minimax problem p ∗ = arg min max Eq ln p p q f (y|θˆ ) , p(y) the worst case of the expected regret is considered instead of the regret for the observed data set, see [32].

This will lead to an excessive code length that is often called regret. The Kullback-Leibler divergence D( p f ) may be used as a starting point where p is employed to approximate the distribution f. Finding the optimal universal distribution p ∗ can be formulated as a minimax problem: p ∗ = arg min max ln p p q f (y|θˆ ) , p(y) where q denotes the set of all feasible distributions. The distribution q(y) represents the worst case approximation of the maximum likelihood code and it is allowed to be almost any distribution within a model class of feasible distributions.

9) BIC(m) = −2 m (θ|y) Let us recall the assumptions for the above result. First of all, the log-likelihood function ought to be a C 2 -function around the neighborhood of the unique MLE θˆ . Second, the prior density is assumed to verify Assumption (A). 8) to hold. , N is assumed to be sufficiently large. Naturally, how large N is required, depends on the application at hand. The benefit of such an approximation is its simplicity and good performance observed in many applications. 9). This sometimes leads to misunderstanding that MDL and BIC are the same.