random coding theorem

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• Shannon's source coding theorem — In information theory, Shannon s source coding theorem (or noiseless coding theorem) establishes the limits to possible data compression, and the operational meaning of the Shannon entropy.The source coding theorem shows that (in the limit, as… …   Wikipedia

• Noisy-channel coding theorem — In information theory, the noisy channel coding theorem (sometimes Shannon s theorem), establishes that for any given degree of noise contamination of a communication channel, it is possible to communicate discrete data (digital information)… …   Wikipedia

• Shannon–Hartley theorem — In information theory, the Shannon–Hartley theorem tells the maximum rate at which information can be transmitted over a communications channel of a specified bandwidth in the presence of noise. It is an application of the noisy channel coding… …   Wikipedia

• Huffman coding — Huffman tree generated from the exact frequencies of the text this is an example of a huffman tree . The frequencies and codes of each character are below. Encoding the sentence with this code requires 135 bits, as opposed of 288 bits if 36… …   Wikipedia

• Distributed source coding — (DSC) is an important problem in information theory and communication. DSC problems regard the compression of multiple correlated information sources that do not communicate with each other.[1] By modeling the correlation between multiple sources …   Wikipedia

• Network coding — is a technique where, instead of simply relaying the packets of information they receive, the nodes of a network will take several packets and combine them together for transmission. This can be used to attain the maximum possible information… …   Wikipedia

• Nyquist–Shannon sampling theorem — Fig.1: Hypothetical spectrum of a bandlimited signal as a function of frequency The Nyquist–Shannon sampling theorem, after Harry Nyquist and Claude Shannon, is a fundamental result in the field of information theory, in particular… …   Wikipedia

• Information theory — Not to be confused with Information science. Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental… …   Wikipedia

• Asymptotic equipartition property — In information theory the asymptotic equipartition property (AEP) is a general property of the output samples of a stochastic source. It is fundamental to the concept of typical set used in theories of compression.Roughly speaking, the theorem… …   Wikipedia

• Statistical inference — In statistics, statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation.[1] More substantially, the terms statistical inference,… …   Wikipedia

• Entropy (information theory) — In information theory, entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information… …   Wikipedia