Probability Tutorials: Theorems 121-140

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Probability Tutorials

121-140

Theorems

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Contents

	Theorem 121:	Jacobian formula 1 (non-negative case)
	Theorem 122:	Jacobian formula 2 (L1 case)
	Theorem 123:	Reduced normal (gaussian) density
	Theorem 124:	Fourier tramsform of reduced normal distribution
	Theorem 125:	Absolute continuity of convolution
	Theorem 126:	Uniqueness of narrow limit of complex measures
	Theorem 127:	Narrow continuity of convolution
	Theorem 128:	Injectivity of fourier transform
	Theorem 129:	Characterictic function determines distribution
	Theorem 130:	Moments of measure from fourier transform
	Theorem 131:	Diagonalisation of symmetric non-negative matrix
	Theorem 132:	Fourier transform of gaussian measure
	Theorem 133:	Gaussian measure has moments of all order
	Theorem 134:	Mean and covariance of gaussian measure
	Theorem 135:	Characteristic function of gaussian vector
	Theorem 136:	Mean and covariance of gaussian vector
	Theorem 137:	Characteristic function of normal random variable
	Theorem 138:	Linear transformation of gaussian vector is gaussian
	Theorem 139:	Gaussian vector criterion in terms of coordinates
	Theorem 140:	Density of gaussian measure

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Theorems 1-20
Theorems 21-40
Theorems 41-60
Theorems 61-80
Theorems 81-100
Theorems 101-120
Theorems 121-140