큰 수의 법칙

노트

위키데이터

• ID : Q207952

말뭉치

1. The law of large numbers is closely related to what is commonly called the law of averages.
2. In coin tossing, the law of large numbers stipulates that the fraction of heads will eventually be close to 1/ 2 .
3. Yet the law of large numbers requires no such mystical force.
4. Swiss commemorative stamp of mathematician Jakob Bernoulli, issued 1994, displaying the formula and the graph for the law of large numbers, first proved by Bernoulli in 1713.
5. In this section, we state and prove the weak law of large numbers (WLLN).
6. The strong law of large numbers is discussed in Section 7.2.
7. The law of large numbers, in probability and statistics, states that as a sample size grows, its mean gets closer to the average of the whole population.
8. In the 16th century, mathematician Gerolama Cardano recognized the Law of Large Numbers but never proved it.
9. In a financial context, the law of large numbers indicates that a large entity which is growing rapidly cannot maintain that growth pace forever.
10. In statistical analysis, the law of large numbers can be applied to a variety of subjects.
11. In statistics and probability theory, the law of large numbers is a theorem that describes the result of repeating the same experiment a large number of times.
12. The law of large numbers is an important concept in statisticsBasic Statistics Concepts for FinanceA solid understanding of statistics is crucially important in helping us better understand finance.
13. The simplest example of the law of large numbers is rolling the dice.
14. In finance, the law of large numbers features a different meaning from the one in statistics.
15. A.A. Markov noted the possibility of further extensions and proposed to apply the term "law of large numbers" to all extensions of the Bernoulli theorem (and, in particular, to (3)).
16. Subsequent proofs of the law of large numbers are all, to varying extents, developments of Chebyshev's method.
17. It is possible to formulate more or less final versions of the law of large numbers for sums of independent random variables.
18. The most important cases to which the law of large numbers does not apply involve the times of return to the starting point in a random walk.
19. We prove a strong law of large numbers for a class of such iterations.
20. An illustration of the law of large numbers using a particular run of rolls of a single die .
21. In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times.
22. It follows from the law of large numbers that the empirical probability of success in a series of Bernoulli trials will converge to the theoretical probability.
23. Therefore, according to the law of large numbers, the proportion of heads in a "large" number of coin flips "should be" roughly ​1⁄ 2 .
24. the strong law of large numbers) is a result in probability theory also known as Bernoulli's theorem.
25. Given that the fuzzy random variable is defined on the basis of random sets, in this paper, we generalize the strong law of large numbers for random sets in the fuzzy metric space.
26. The strong law of large numbers (SLLN) for random sets and fuzzy random variables in the Pompeiu–Hausdorff metric and the generalized Pompeiu–Hausdorff metric has been studied since 1982.
27. The law of large numbers (LLN, 대수의 법칙/大數 法則) is a probability theorem that describes the result of performing the same experiment a large number of times.
28. The LLN is important because it "guarantees" stable long-term results for the averages of some random events.
29. It is important to remember that the LLN only applies (as the name indicates) when a large number of observations are considered.
30. # demonstrate the law of large numbers from numpy .
31. too must suffer the law of large numbers.
32. The Law of Large Numbers (LLN) is one of the single most important theorem’s in Probability Theory.
33. It’s worth mentioning that there are variants of the LLN that allow relaxation of the i.i.d. requirement.
34. The law of large numbers may explain why, even at its recent lofty stock price, Apple looks like a bargain by most measures.
35. The goal of this paper is to establish the strong law of large numbers for a sequence of END and identically distributed random variables.
36. In probability and statistics, the law of large numbers states that as a sample size starts to grow its mean comes closer to the average of the entire population.
37. In a financial sense, the law of large numbers shows that a large company that is rapidly expanding cannot sustain the rate of growth forever.
38. The law of large numbers can be used in statistical analysis on a variety of subjects.
39. The law of large numbers doesn't mean that a given sample or group of successive samples will always represent the true characteristics of the population, especially for small samples.
40. In some economic models, the following conditional law of large numbers (LLN) is requested.
41. If is not trivial and the sigma-field C countably generated, the conditional LLN fails in the usual (countably additive) setting.
42. The Law of Large Numbers is a theorem that guarantees the stability of long-term averages of random events, but is valid only for Euclidean metrics based on-norms.
43. The purpose of this paper is to extend the theorem of the “Law of Large Numbers” to the non-Euclidean,-means.