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[1] 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 ...
The physical entropy may be on a "per quantity" basis (h) which is called "intensive" entropy instead of the usual total entropy which is called "extensive" entropy. The "shannons" of a message ( Η ) are its total "extensive" information entropy and is h times the number of bits in the message.
Standard entropy of 1 mole of graphite [2] 10 33: ≈ 10 35 J⋅K −1: Entropy of the Sun (given as ≈ 10 42 erg⋅K −1 in Bekenstein (1973)) [3] 10 54: 1.5 × 10 54 J⋅K −1: Entropy of a black hole of one solar mass (given as ≈ 10 60 erg⋅K −1 in Bekenstein (1973)) [3] 10 81: 4.3 × 10 81 J⋅K −1: One estimate of the theoretical ...
1.1 × 10 25 bits – entropy increase of 1 mole (18.02 g) of water, on vaporizing at 100 °C at standard pressure; equivalent to an average of 18.90 bits per molecule. [24] 1.5 × 10 25 bits – information content of 1 mole (20.18 g) of neon gas at 25 °C and 1 atm; equivalent to an average of 25.39 bits per atom. [25] 2 86: 10 26: 2 89: 10 ...
When = /, the binary entropy function attains its maximum value, 1 shannon (1 binary unit of information); this is the case of an unbiased coin flip. When p = 0 {\displaystyle p=0} or p = 1 {\displaystyle p=1} , the binary entropy is 0 (in any units), corresponding to no information, since there is no uncertainty in the variable.
The Sackur–Tetrode constant, written S 0 /R, is equal to S/k B N evaluated at a temperature of T = 1 kelvin, at standard pressure (100 kPa or 101.325 kPa, to be specified), for one mole of an ideal gas composed of particles of mass equal to the atomic mass constant (m u = 1.660 539 068 92 (52) × 10 −27 kg [5]).
Assume that the combined system determined by two random variables and has joint entropy (,), that is, we need (,) bits of information on average to describe its exact state. Now if we first learn the value of X {\displaystyle X} , we have gained H ( X ) {\displaystyle \mathrm {H} (X)} bits of information.
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]