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In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution. This holds even if the original variables themselves are not normally distributed. There are several versions of the CLT, each applying in the ...
The central limit theorem also implies that certain distributions can be approximated by the normal distribution, for example: The binomial distribution B ( n , p ) {\textstyle B(n,p)} is approximately normal with mean n p {\textstyle np} and variance n p ( 1 − p ) {\textstyle np(1-p)} for large n {\displaystyle n} and for p ...
Upload file; Special pages; Search ... bound on estimates of the population mean, in light of the central limit theorem. ... formula given above for the standard ...
Then according to the central limit theorem, the distribution of Z n approaches the normal N(0, 1 / 3 ) distribution. This convergence is shown in the picture: as n grows larger, the shape of the probability density function gets closer and closer to the Gaussian curve.
Feller's theorem can be used as an alternative method to prove that Lindeberg's condition holds. [5] Letting := = and for simplicity [] =, the theorem states . if >, (| | >) = and converges weakly to a standard normal distribution as then satisfies the Lindeberg's condition.
This is justified by considering the central limit theorem in the log domain (sometimes called Gibrat's law). The log-normal distribution is the maximum entropy probability distribution for a random variate X —for which the mean and variance of ln(X) are specified. [5]
Berry–Esséen theorem; Berry–Esséen theorem; De Moivre–Laplace theorem; Lyapunov's central limit theorem; Misconceptions about the normal distribution; Martingale central limit theorem; Infinite divisibility (probability) Method of moments (probability theory) Stability (probability) Stein's lemma; Characteristic function (probability ...
In probability theory, an empirical process is a stochastic process that characterizes the deviation of the empirical distribution function from its expectation. In mean field theory, limit theorems (as the number of objects becomes large) are considered and generalise the central limit theorem for empirical measures.