Difference between revisions of "Information theory"

Revision as of 12:44, 9 September 2015

Information theory is a branch of applied mathematics, electrical engineering, and computer science involving the quantification of information.

(TO DO: expand, organize, cross-reference, illustrate.)

Description

Information theory was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as:

• Compressing data
• Reliably storing and communicating data

Applications

Since its inception it has broadened to find applications in many other areas, including:

• Statistical inference
• Natural language processing
• Cryptography
• Neurobiology
• The evolution and function of molecular codes
• Model selection in ecology
• Thermal physics
• Quantum computing
• Linguistics
• Plagiarism detection
• Pattern recognition
• Anomaly detection
• Other forms of data analysis

Entropy

A key measure of information is entropy, which is usually expressed by the average number of bits needed to store or communicate one symbol in a message.

Entropy quantifies the uncertainty involved in predicting the value of a random variable. For example, specifying the outcome of a fair coin flip (two equally likely outcomes) provides less information (lower entropy) than specifying the outcome from a roll of a die (six equally likely outcomes).

See also

• Artificial intelligence
• Computing
• Entropy
• Information science
• Pattern recognition
• Shannon, Claude E.
• Telecommunication
• Units of information

External links

• Information theory @ Wikipedia