mcmc2 - Contents of Lecture III 1. The Central Limit...

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Unformatted text preview: Contents of Lecture III 1. The Central Limit Theorem and Binning 2. Gaussian Error Analysis for Large and Small Samples 3. The Jackknife Approach 1 The Central Limit Theorem and Binning How is the sum of two independent random variables y r = x r 1 + x r 2 . (1) distributed? We denote the probability density of y r by g ( y ) . The corresponding cumulative distribution function is given by G ( y ) = Z x 1 + x 2 y f 1 ( x 1 ) f 2 ( x 2 ) dx 1 dx 2 = Z + - f 1 ( x ) F 2 ( y- x ) dx where F 2 ( x ) is the distribution function of the random variable x r 2 . We take the derivative and obtain the probability density of y r g ( y ) = dG ( y ) dy = Z + - f 1 ( x ) f 2 ( y- x ) dx . (2) 2 The probability density of a sum of two independent random variables is the convolution of the probability densities of these random variables. Example: Sums of uniform random numbers, corresponding to the sums of an uniformly distributed random variable x r (0 , 1] : (a) Let y r = x r + x r , then g 2 ( y ) = y for y 1 , 2- y for 1 y 2 , elsewhere . (3) (b) Let y r = x r + x r + x r , then g 3 ( y ) = y 2 / 2 for 0 y 1 , (- 2 y 2 + 6 y- 3) / 2 for 1 y 2 , ( y- 3) 2 / 2 for 2 y 3 , elsewhere . (4) 3 The convolution (2) takes on a simple form in Fourier space . In statistics the Fourier transformation of the probability density is known as characteristic function , defined as the expectation value of e itx r : ( t ) = h e itx r i = Z + - e itx f ( x ) dx . (5) The characteristic function is particularly useful for investigating sums of random variables, y r = x r 1 + x r 2 : y ( t ) = D e itx r 1 + itx r 2 E = Z + - Z + - e itx 1 e itx 2 f 1 ( x 1 ) f 2 ( x 2 ) dx 1 dx 2 = x 1 ( t ) x 2 ( t ) . The characteristic function of a sum of random variables is the product of their characteristic functions. The result generalizes immediately to N random variables y r = x r 1 + ... + x r N . (6) 4 The characteristic function of y r is y ( t ) = N Y i =1 x i ( t ) (7) and the probability density of y r is the Fourier back-transformation of this characteristic function g ( y ) = 1 2 Z + - dte- ity y ( t ) . (8) The probability density of the sample mean is obtained as follows: The arithmetic mean of y r is x r = y r /N . We denote the probability density of y r by g N ( y ) and the probability density of the arithmetic mean by b g N ( x ) . They are related by b g N ( x ) = N g N ( N x ) . (9) 5 This follows by substituting y = N x into g N ( y ) dy : 1 = Z + - g N ( y ) dy = Z + - g N ( N x ) 2 d x = Z + - b g N ( x ) d x . Example: 0.5 1 1.5 2 2.5 0.2 0.4 0.6 0.8 1 g x g 2 (x) g 3 (x) Figure 1: Probability densities for the arithmetic means of two and three uniformly distributed random variables, b g 2 ( x ) and b g 3 ( x ) , respectively....
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This note was uploaded on 11/10/2011 for the course PHY 5157 taught by Professor Berg during the Fall '08 term at University of Florida.

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mcmc2 - Contents of Lecture III 1. The Central Limit...

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