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Basic Probability Reference Sheet
Basic Probability Reference Sheet

The strong law of large numbers - University of California, Berkeley
The strong law of large numbers - University of California, Berkeley

... A well known unsolved problem in the theory of probability is to find a set of necessary and sufficient conditions (nasc's) for the validity of the strong law of large numbers (SLLN) for a sequence of independent random variables. This problem will not be solved in the present paper. To avoid a poss ...
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... Let X be an integer in the range 10..20. P(x) = 1/11 for x = 10,11,12,13,14,..20 To find E(X): Let Y be a number in the range 1..11. Thus, E(X) = (1+11)/2 = 6. Now, X = Y + 9. E(X) = E(Y+9) = E(Y) + 9 = 6+9 = 15 Var(X) = Var(Y+9) = Var(Y) = (112 – 1)/12 = 10 Note: the Discrete Uniform Distribution i ...
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1. If f(x) = 4x 2- 2x - 1, then f (1/2) equals a. 1 b.

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... a. Consists of the collection, organization, summarization and presentation of data. b. Consists of generalizing from a sample to populating, performing estimations and hypothesis tests, determining relationships among variables, and making predictions. c. Proves relationships are true. d. Both A an ...
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1 Introduction 2 Binary shift map - University of Helsinki Confluence

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Simulation of Random Walk

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Law of large numbers



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. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed.The LLN is important because it ""guarantees"" stable long-term results for the averages of some random events. For example, while a casino may lose money in a single spin of the roulette wheel, its earnings will tend towards a predictable percentage over a large number of spins. Any winning streak by a player will eventually be overcome by the parameters of the game. It is important to remember that the LLN only applies (as the name indicates) when a large number of observations are considered. There is no principle that a small number of observations will coincide with the expected value or that a streak of one value will immediately be ""balanced"" by the others (see the gambler's fallacy)
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