Index of information theory articles and Kullback–Leibler divergence

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Difference between Index of information theory articles and Kullback–Leibler divergence

Index of information theory articles vs. Kullback–Leibler divergence

This is a list of information theory topics, by Wikipedia page. In mathematical statistics, the Kullback–Leibler divergence (also called relative entropy) is a measure of how one probability distribution diverges from a second, expected probability distribution.

Conditional entropy

In information theory, the conditional entropy (or equivocation) quantifies the amount of information needed to describe the outcome of a random variable Y given that the value of another random variable X is known.

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Cross entropy

In information theory, the cross entropy between two probability distributions p and q over the same underlying set of events measures the average number of bits needed to identify an event drawn from the set, if a coding scheme is used that is optimized for an "unnatural" probability distribution q, rather than the "true" distribution p. The cross entropy for the distributions p and q over a given set is defined as follows: where H(p) is the entropy of p, and D_(p \| q) is the Kullback–Leibler divergence of q from p (also known as the relative entropy of p with respect to q — note the reversal of emphasis).

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Data compression

In signal processing, data compression, source coding, or bit-rate reduction involves encoding information using fewer bits than the original representation.

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Entropy (information theory)

Information entropy is the average rate at which information is produced by a stochastic source of data.

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Huffman coding

In computer science and information theory, a Huffman code is a particular type of optimal prefix code that is commonly used for lossless data compression.

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Principle of maximum entropy

The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge is the one with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information).

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Quantum information science

Quantum information science is an area of study based on the idea that information science depends on quantum effects in physics.

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Rényi entropy

In information theory, the Rényi entropy generalizes the Hartley entropy, the Shannon entropy, the collision entropy and the min entropy.

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Self-information

In information theory, self-information or surprisal is the surprise when a random variable is sampled.

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