Publication

The Chaining Lemma and Its Application

Sebastian Faust
2015
Conference Papers

Abstract

We present a new information-theoretic result which we call the Chaining Lemma. It considers a so-called "chain" of random variables, defined by a source distribution X-(0) with high min-entropy and a number (say, t in total) of arbitrary functions (T-1,...,T-t) which are applied in succession to that source to generate the chain X-(0) (sic) X-(1) (sic) X-(2)...(sic) X-(t). Intuitively, the Chaining Lemma guarantees that, if the chain is not too long, then either (i) the entire chain is "highly random", in that every variable has high min-entropy; or (ii) it is possible to find a point j (1

Official source

https://infoscience.epfl.ch/entities/publication/69ee4a9c-84ea-43f0-8428-351e1ec175c1

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Sebastian Faust
2015
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/69ee4a9c-84ea-43f0-8428-351e1ec175c1

About this result

Related concepts (32)

Information theory

Information theory is the mathematical study of the quantification, storage, and communication of information. The field was originally established by the works of Harry Nyquist and Ralph Hartley, in the 1920s, and Claude Shannon in the 1940s. The field, in applied mathematics, is at the intersection of probability theory, statistics, computer science, statistical mechanics, information engineering, and electrical engineering. A key measure in information theory is entropy.

Random variable

A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events. The term 'random variable' can be misleading as it is not actually random nor a variable, but rather it is a function from possible outcomes (e.g., the possible upper sides of a flipped coin such as heads and tails ) in a sample space (e.g., the set ) to a measurable space (e.g., in which 1 corresponding to and −1 corresponding to ), often to the real numbers.

Complex random variable

In probability theory and statistics, complex random variables are a generalization of real-valued random variables to complex numbers, i.e. the possible values a complex random variable may take are complex numbers. Complex random variables can always be considered as pairs of real random variables: their real and imaginary parts. Therefore, the distribution of one complex random variable may be interpreted as the joint distribution of two real random variables.

Related publications (32)

The Chaining Lemma and Its Application

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