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The Interplay Between Information and Estimation Measures (Paperback)
Loot Price: R2,343
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The Interplay Between Information and Estimation Measures (Paperback)
Series: Foundations and Trends (R) in Signal Processing
Expected to ship within 10 - 15 working days
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If information theory and estimation theory are thought of as two
scientific languages, then their key vocabularies are information
measures and estimation measures, respectively. The basic
information measures are entropy, mutual information and relative
entropy. Among the most important estimation measures are mean
square error (MSE) and Fisher information. Playing a paramount role
in information theory and estimation theory, those measures are
akin to mass, force and velocity in classical mechanics, or energy,
entropy and temperature in thermodynamics. The Interplay Between
Information and Estimation Measures is intended as handbook of
known formulas which directly relate to information measures and
estimation measures. It provides intuition and draws connections
between these formulas, highlights some important applications, and
motivates further explorations. The main focus is on such formulas
in the context of the additive Gaussian noise model, with lesser
treatment of others such as the Poisson point process channel. Also
included are a number of new results which are published here for
the first time. Proofs of some basic results are provided, whereas
many more technical proofs already available in the literature are
omitted. In 2004, the authors of this monograph found a general
differential relationship commonly referred to as the I-MMSE
formula. In this book a new, complete proof for the I-MMSE formula
is developed, which includes some technical details omitted in the
original papers relating to this. It concludes by highlighting the
impact of the information-estimation relationships on a variety of
information-theoretic problems of current interest, and provide
some further perspective on their applications.
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