Probability distributions

Gaussian distribution on a locally compact Abelian group

Gaussian distribution on a locally compact Abelian group is a distribution on a secondcountable locally compact Abelian group which satisfies theconditions: (i) is an infinitely divisible distribution; (ii) if , where is the generalizedPoisson distribution, associated with a finite measure , and is an infinitely divisible distribution, then the measure is degenerated at zero. This definition of the Gaussian distribution for the group coincides with the classical one. The support ofa Gaussian distribution is a coset of a connected subgroup of . Let be the character group of the group . A distribution on is Gaussian if and only if itscharacteristic function can be represented in the form , where is thevalue of a character at an element , and is a continuous nonnegative function on satisfyingthe equation . A Gaussian distribution is called symmetric if . Denote by the set of Gaussian distributions on the group , and by the set of symmetric Gaussian distribution on. If , then is a continuoushomomorphic image of a Gaussian distribution in a real linear space.This space is either finite dimensional or infinite dimensional(the space of all sequences of real numbers in the producttopology). If a distribution can be embedded in a continuousone-parameter semigroup , of distributions on, then if and only if for any neighbourhood of zero in the group. Let be a connected group, and. If is not a locally connected, then is singular (with respect of a Haar distribution on ). If is a locally connected and has a finitedimension, then is either absolutely continuous orsingular. The question of the validity of a similar statement onlocally connected groups of infinite dimension is open, although onsuch groups it is possible to construct both absolutely continuousand singular Gaussian distributions. It is well known that two Gaussian distributions in a linear spaceare either mutually absolutely continuous or mutually singular. Thisalternative is true for Gaussian distributions on connected groupsof finite dimension. The following theorem is valid, which can be consideredas an analogue of Cramer's theorem on the decomposition of the normal distribution for locally compact Abelian groups. (Wikipedia).

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(PP 6.8) Marginal distributions of a Gaussian

For any subset of the coordinates of a multivariate Gaussian, the marginal distribution is multivariate Gaussian.

From playlist Probability Theory

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Multivariate Gaussian distributions

Properties of the multivariate Gaussian probability distribution

From playlist cs273a

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Gaussian/Normal Distributions

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From playlist Probability

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(ML 7.10) Posterior distribution for univariate Gaussian (part 2)

Computing the posterior distribution for the mean of the univariate Gaussian, with a Gaussian prior (assuming known prior mean, and known variances). The posterior is Gaussian, showing that the Gaussian is a conjugate prior for the mean of a Gaussian.

From playlist Machine Learning

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(ML 7.9) Posterior distribution for univariate Gaussian (part 1)

Computing the posterior distribution for the mean of the univariate Gaussian, with a Gaussian prior (assuming known prior mean, and known variances). The posterior is Gaussian, showing that the Gaussian is a conjugate prior for the mean of a Gaussian.

From playlist Machine Learning

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(PP 6.3) Gaussian coordinates does not imply (multivariate) Gaussian

An example illustrating the fact that a vector of Gaussian random variables is not necessarily (multivariate) Gaussian.

From playlist Probability Theory

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(PP 6.9) Conditional distributions of a Gaussian

For any subset of the coordinates of a multivariate Gaussian, the conditional distribution (given the remaining coordinates) is multivariate Gaussian.

From playlist Probability Theory

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Uniform Probability Distribution Examples

Overview and definition of a uniform probability distribution. Worked examples of how to find probabilities.

From playlist Probability Distributions

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From playlist Mathematics

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From playlist HIM Lectures 2015

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From playlist Analysis and Beyond

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From playlist Infosys-ICTS Ramanujan Lectures

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(PP 6.4) Density for a multivariate Gaussian - definition and intuition

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From playlist Probability Theory

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Nexus Trimester - Mokshay Madiman (University of Delaware)

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From playlist Centro di Ricerca Matematica Ennio De Giorgi

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From playlist High Energy Theory

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From playlist Advanced Data Science

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From playlist Panorama of Mathematics

Related pages

Locally connected space | Haar measure | Multivariate normal distribution | One-parameter group | Cramér's decomposition theorem | Probability distribution | Singular measure | Abelian group