Information theory | Wikipedia audio article
Channel: wikipedia tts
Date: 25/12/2018

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Information_theory


00:02:07 1 Overview
00:04:33 2 Historical background
00:05:24 3 Quantities of information
00:07:47 3.1 Entropy of an information source
00:09:36 3.2 Joint entropy
00:13:06 3.3 Conditional entropy (equivocation)
00:14:07 3.4 Mutual information (transinformation)
00:15:33 3.5 Kullback–Leibler divergence (information gain)
00:17:41 3.6 Other quantities
00:21:57 4 Coding theory
00:24:50 4.1 Source theory
00:25:14 4.1.1 Rate
00:27:02 4.2 Channel capacity
00:27:35 4.2.1 Capacity of particular channel models
00:29:38 5 Applications to other fields
00:31:40 5.1 Intelligence uses and secrecy applications
00:32:37 5.2 Pseudorandom number generation
00:32:47 5.3 Seismic exploration
00:34:36 5.4 Semiotics
00:35:39 5.5 Miscellaneous applications
00:36:10 6 See also
00:37:00 6.1 Applications
00:37:17 6.2 History
00:37:54 6.3 Theory
00:38:03 6.4 Concepts
00:38:11 7 References



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- Socrates



SUMMARY
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Information theory studies the quantification, storage, and communication of information.  It was originally proposed by Claude E. Shannon in 1948 to find fundamental limits on signal processing and communication operations such as data compression, in a landmark paper entitled "A Mathematical Theory of Communication". Applications of fundamental topics of information theory include lossless data compression (e.g. ZIP files), lossy data compression (e.g. MP3s and JPEGs), and channel coding (e.g. for DSL). Its impact has been crucial to the success of the Voyager missions to deep space, the invention of the compact disc, the feasibility of mobile phones, the development of the Internet, the study of linguistics and of human perception, the understanding of black holes, and numerous other fields.
A key measure in information theory is "entropy". Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. For example, identifying the outcome of a fair coin flip (with two equally likely outcomes) provides less information (lower entropy) than specifying the outcome from a roll of a die (with six equally likely outcomes). Some other important measures in information theory are mutual information, channel capacity, error exponents, and relative entropy.
The field is at the intersection of mathematics, statistics, computer science, physics, neurobiology, information engineering, and electrical engineering. The theory has also found applications in other areas, including statistical inference, natural language processing, cryptography, neurobiology, human vision, the evolution and function of molecular codes (bioinformatics), model selection in statistics, thermal physics, quantum computing, linguistics, plagiarism detection, pattern recognition, and anomaly detection. Important sub-fields of information theory include source coding, channel coding, algorithmic complexity theory, algorithmic information theory, information-theoretic security, and measures of information.