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Two bits of entropy: In the case of two fair coin tosses, the information entropy in bits is the base-2 logarithm of the number of possible outcomes — with two coins there are four possible outcomes, and two bits of entropy. Generally, information entropy is the average amount of information conveyed by an event, when considering all ...
Binary entropy is a special case of (), the entropy function. H ( p ) {\displaystyle \operatorname {H} (p)} is distinguished from the entropy function H ( X ) {\displaystyle \mathrm {H} (X)} in that the former takes a single real number as a parameter whereas the latter takes a distribution or random variable as a parameter.
Common values of b are 2, Euler's number e, and 10, and the unit of entropy is shannon (or bit) for b = 2, nat for b = e, and hartley for b = 10. [ 1 ] Mathematically H may also be seen as an average information, taken over the message space, because when a certain message occurs with probability p i , the information quantity −log( p i ...
The ideal elements by nature have an entropy value of n. The inputs of the conditioning function will need to have a higher min-entropy value H to satisfy the full-entropy definition. The number of additional bits of entropy depends on W and δ; the following table contains few representative values: [4]
Although bit is more frequently used in place of shannon, its name is not distinguished from the bit as used in data processing to refer to a binary value or stream regardless of its entropy (information content). Other units include the nat, based on the natural logarithm, and the hartley, based on the base 10 or common logarithm.
9.5699 × 10 −24 J⋅K −1: Entropy equivalent of one bit of information, equal to k times ln(2) [1] 10 −23: 1.381 × 10 −23 J⋅K −1: Boltzmann constant, entropy equivalent of one nat of information. 10 1: 5.74 J⋅K −1: Standard entropy of 1 mole of graphite [2] 10 33: ≈ 10 35 J⋅K −1: Entropy of the Sun (given as ≈ 10 42 ...
Landauer's principle is a physical principle pertaining to a lower theoretical limit of energy consumption of computation.It holds that an irreversible change in information stored in a computer, such as merging two computational paths, dissipates a minimum amount of heat to its surroundings. [1]
An entropy coding attempts to approach this lower bound. Two of the most common entropy coding techniques are Huffman coding and arithmetic coding. [2] If the approximate entropy characteristics of a data stream are known in advance (especially for signal compression), a simpler static code may be useful.