In probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such that...

machine learning, Gaussian process approximation is a computational method that accelerates inference tasks in the context of a Gaussian process model, most...

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 one-dimensional GRF is also called a Gaussian process. An important special case of a GRF is the Gaussian free field. With regard to applications of...

In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician...

Kriging, (/ˈkriːɡɪŋ/) also known as Gaussian process regression, is a method of interpolation based on Gaussian process governed by prior covariances. Under...

In mathematics, a Gaussian function, often simply referred to as a Gaussian, is a function of the base form f ( x ) = exp ⁡ ( − x 2 ) {\displaystyle f(x)=\exp(-x^{2})}...

In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf)...

This model is called a Gaussian white noise signal (or process). In the mathematical field known as white noise analysis, a Gaussian white noise w {\displaystyle...

q-Gaussian processes are deformations of the usual Gaussian distribution. There are several different versions of this; here we treat a multivariate deformation...

The Ornstein–Uhlenbeck process is a stationary Gauss–Markov process, which means that it is a Gaussian process, a Markov process, and is temporally homogeneous...

influence diagrams. A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate...

Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature...

Wiener process is used to represent the integral of a white noise Gaussian process, and so is useful as a model of noise in electronics engineering (see...

computer hours [3]. The typical model for a computer code output is a Gaussian process. For notational simplicity, assume f ( x ) {\displaystyle f(x)} is...

splines smoothing splines neural networks In Gaussian process regression, also known as Kriging, a Gaussian prior is assumed for the regression curve. The...

The normal-inverse Gaussian distribution (NIG, also known as the normal-Wald distribution) is a continuous probability distribution that is defined as...

In statistics, Gaussian process emulator is one name for a general type of statistical model that has been used in contexts where the problem is to make...

prior is a Gaussian process as this allows us to obtain a closed-form posterior distribution on the integral which is a univariate Gaussian distribution...

electronics and signal processing, mainly in digital signal processing, a Gaussian filter is a filter whose impulse response is a Gaussian function (or an approximation...

constant. Gaussian process is a powerful non-linear interpolation tool. Many popular interpolation tools are actually equivalent to particular Gaussian processes...

Finite-dimensional distribution First passage time Galton–Watson process Gamma process Gaussian process – a process where all linear combinations of coordinates are normally...

In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued...

increments of fBm need not be independent. fBm is a continuous-time Gaussian process B H ( t ) {\textstyle B_{H}(t)} on [ 0 , T ] {\textstyle [0,T]} , that...

between the data is assumed, and each data source is assumed to be a Gaussian process, this constitutes a non-linear Bayesian regression problem. See also...

independent gaussian random processes with zero mean; the dynamic systems will be linear." Regardless of Gaussianity, however, if the process and measurement...

In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of...

peaks in rapid succession before the process reverts to its mean. Consider a scalar, zero-mean Gaussian process y(t) with variance σy2 and power spectral...

of statistical analysis software that allows doing inference with Gaussian processes often using approximations. This article is written from the point...

{\displaystyle \varepsilon _{t}} is a Gaussian process then X t {\displaystyle X_{t}} is also a Gaussian process. In other cases, the central limit theorem...