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The distribution of a Gaussian process is the joint distribution of all those (infinitely many) random variables, and as such, it is a distribution over functions with a continuous domain, e.g. time or space.
A Neural Network Gaussian Process (NNGP) is a Gaussian process (GP) obtained as the limit of a certain type of sequence of neural networks.Specifically, a wide variety of network architectures converges to a GP in the infinitely wide limit, in the sense of distribution.
A log-normal process is the statistical realization of the multiplicative product of many ... such as dB or neper, has a normal (i.e., Gaussian) distribution." [90 ...
For a Gaussian process, all sets of values have a multidimensional Gaussian distribution. Analogously, X ( t ) {\displaystyle X(t)} is a Student t process on an interval I = [ a , b ] {\displaystyle I=[a,b]} if the correspondent values of the process X ( t 1 ) , …
The chi-squared distribution, which is the sum of the squares of n independent Gaussian random variables. It is a special case of the Gamma distribution, and it is used in goodness-of-fit tests in statistics. The inverse-chi-squared distribution; The noncentral chi-squared distribution; The scaled inverse chi-squared distribution; The Dagum ...
In statistics, a Gaussian random field (GRF) is a random field involving Gaussian probability density functions of the variables. A one-dimensional GRF is also called a Gaussian process . An important special case of a GRF is the Gaussian free field .
In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional normal distribution to higher dimensions.
In statistics and machine learning, Gaussian process approximation is a computational method that accelerates inference tasks in the context of a Gaussian process model, most commonly likelihood evaluation and prediction. Like approximations of other models, they can often be expressed as additional assumptions imposed on the model, which do ...