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위키데이터

• ID : Q204570

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1. A formal way of putting that is to say the game of Russian roulette has more ‘entropy’ than crossing the street.
2. Above is the formula for calculating the entropy of a probability distribution.
3. The English language has 26 letters, if you assume each letter has a probability of 1/26 of being next, the language has an entropy of 4.7 bits.
4. Experiments by Shannon showed that English has an entropy between 0.6 and 1.3 bits.
5. In addition, we calculate the Kullback-Leibler relative entropy, the Jensen-Shannon divergence, Onicescu’s information energy, and a complexity measure recently proposed.
6. Calculating the information for a random variable is called “information entropy,” “Shannon entropy,” or simply “entropy“.
7. the Shannon entropy of a distribution is the expected amount of information in an event drawn from that distribution.
8. The lowest entropy is calculated for a random variable that has a single event with a probability of 1.0, a certainty.
9. We can consider a roll of a fair die and calculate the entropy for the variable.
10. The maximum surprise is for p = 1/2, when there is no reason to expect one outcome over another, and in this case a coin flip has an entropy of one bit.
11. The minimum surprise is when p = 0 or p = 1, when the event is known and the entropy is zero bits.
12. The definition can be derived from a set of axioms establishing that entropy should be a measure of how "surprising" the average outcome of a variable is.
13. If the probability of heads is the same as the probability of tails, then the entropy of the coin toss is as high as it could be for a two-outcome trial.
14. Claude Shannon calls this measure of average uncertainty "entropy", and he uses the letter H to represent it.
15. The unit of entropy Shannon chooses, is based on the uncertainty of a fair coin flip, and he calls this "the bit", which is equivalent to a fair bounce.
16. Entropy or H is the summation for each symbol, of the probability of that symbol times the number of bounces.
17. Entropy or H, is the summation for each symbol of the probability of that symbol times the logarithm base two of one over the probability of that symbol.
18. We have deduced a general formula for information entropy of mixing, which is the change in information entropy when two or more molecules form an ensemble.
19. This implies a striking difference from thermodynamic entropy, which cannot be nonzero when mixing different particles.
20. The information entropy of a molecular ensemble has been expressed through the information entropies of the constituting molecules and exemplified with real chemical systems.
21. This Research Topic is focused on what the characterization of entropy and information could tell us about life and survival.
22. Entropy is commonly interpreted as a measure of disorder.
23. The book explains with minimum amount of mathematics what information theory is and how it is related to thermodynamic entropy.
24. Shannon’s concept of entropy can now be taken up.
25. His thought experiment was intended to demonstrate the possibility of a gas evolving from a higher to a lower entropy state.
26. Although they show the law of entropy increase is not absolute, we might still be surprised to actually witness a large decrease in entropy happen through these means.
27. He argued that in order to achieve the entropy reduction, the intelligent being must acquire knowledge of which fluctuation occurs and so must perform a measurement.
28. Szilard argued that the second law would be saved if the acquisition of knowledge by the demon came with a compensating entropy cost.
29. This is just...entropy, he said, thinking that this explained everything, and he repeated the strange word a few times.
30. We can compute or measure the quantity of energy contained in this sheet of paper, and the same is true of its entropy.
31. Our galaxy, the solar system, and the biosphere all take their being from entropy, as a result of its transferenceto the surrounding medium.
32. This article aims at giving an overview of the scientific production about Entropy and Information Theory in national periodical publications in Qualis/CAPES.
33. The concept of Entropy emerged from Physics and started expanding to many other areas.
34. Mattos and Veiga (2002, p.3) emphasize that "Entropy in Information Theory corresponds to probabilistic uncertainty associated with a probability distribution.
35. For answering this question, we aim at giving an overview of the scientific production on Entropy and Information Theory in articles in periodical publications listed in Qualis/CAPES.
36. The pertinence of information entropy to such diverse fields lies in its ability to encapsulate not only the number of subcategories but also the relative quantities observed.
37. We propose the use of a modified version of the information entropy equation to accurately evaluate dispersity.
38. Information entropy () was first proposed by Claude Shannon in 1948 to quantify the amount of information produced by a given process.
39. The entropy is often described as being analogous to the amount of information conveyed when the outcome of an event is observed.
40. Statistical entropy was introduced by Shannon as a basic concept in information theory measuring the average missing information in a random source.
41. Extended into an entropy rate, it gives bounds in coding and compression theorems.
42. The relevance of entropy beyond the realm of physics, in particular for living systems and ecosystems, is yet to be demonstrated.
43. Thus it is natural that in this case entropy H reduces to variety V. Like variety, H expresses our uncertainty or ignorance about the system's state.
44. Information and entropy theory for the sustainability of coupled human and natural systems.
45. Here, we briefly review feedbacks as they have been observed in CHANS and then discuss the use of information theory to study system entropy and Fisher information.
46. Information is quantifiable as the amount of order or organization provided by a message (Haken 2006); entropy is one measure.
47. In this case, high Shannon entropy actually signals high redundancy in function, therefore conveying resilience to the ecosystem (MacDougall et al.
48. The spatial and temporal complexity of solute transport process was characterized and evaluated using spatial moment and information entropy.
49. Results indicated that the entropy increased as the increase of complexity of solute transport process.
50. For the point source, the one-dimensional entropy of solute concentration increased at first and then decreased along X and Y directions.
51. As time increased, entropy peak value basically unchanged, peak position migrated along the flow direction (X direction) and approximately coincided with the centroid position.
52. The molecules in ice have to stay in a lattice, as it is a rigid system, so ice has low entropy.
53. The molecules in water have more positions to move around, so water in liquid state has medium entropy.
54. To introduce the notion of entropy in probability, we’ll use an example throughout this whole article.
55. In the next section, we’ll cook up a formula for entropy.
56. Herein, we propose the use of information entropy as an alternative and assumption-free method for describing nanoparticle size distributions.
57. The average information entropy of the population \(\bar{H}\), reflects the level of information noise in the entire system.
58. The evolution of the average entropy of the network over time is shown for several different values of β and K in Fig.
59. It can be observed from the Figure that in most cases, an entropy explosion occurs sometime between t = 10 and t = 100, with the average information entropy shooting upward rapidly.
60. In the case where β = −3, the average entropy stabilizes at a higher level than in the other two cases.

메타데이터

위키데이터

• ID : Q204570

Spacy 패턴 목록

• [{'LOWER': 'information'}, {'LEMMA': 'entropy'}]
• [{'LEMMA': 'entropy'}]
• [{'LOWER': 'shannon'}, {'LEMMA': 'entropy'}]