- published: 01 May 2016
- views: 1
In information theory, entropy is a measure of the uncertainty associated with a random variable. In this context, the term usually refers to the Shannon entropy, which quantifies the expected value of the information contained in a message, usually in units such as bits.
Equivalently, the Shannon entropy is a measure of the average information content one is missing when one does not know the value of the random variable. The concept was introduced by Claude E. Shannon in his 1948 paper "A Mathematical Theory of Communication".
Shannon's entropy represents an absolute limit on the best possible lossless compression of any communication, under certain constraints: treating messages to be encoded as a sequence of independent and identically distributed random variables, Shannon's source coding theorem shows that, in the limit, the average length of the shortest possible representation to encode the messages in a given alphabet is their entropy divided by the logarithm of the number of symbols in the target alphabet.[citation needed]
WII 2a Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits
Free Download A FAREWELL TO ENTROPY Statistical Thermodynamics Based on Information by Arieh Ben Na
Philosophy - Why Life Exists
Maxwell's Demon 2 Entropy, Classical and Quantum Information, Computing
Lecture 10 Cryptography and Key Entropy
Lecture 16 Entropy and Microstate Information
Lecture 4 Entropy and the Average Surprise
Information, Entropy, Life and the Universe What We Know and What We Do Not Know
Mineplex Dominate (Competitive) #53 - Wajimbe's Tribe vs "Entropy"
Raymond W. Yeung: Facets of Entropy
Nexus Trimester - František Matúš (Institute of Information Theory and Automation) 2/3
Reduction of Encryption Key Search Space Based on The Min-entropy Approach