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

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...

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

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

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...

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...

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...

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...

Bayesian framework, kernel methods serve as a fundamental component of Gaussian processes, where the kernel function operates as a covariance function that...

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...

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

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

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

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

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...

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

most common choice of prior distribution for f {\displaystyle f} is a Gaussian process as this permits conjugate inference to obtain a closed-form posterior...

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...

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...

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...

{\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...

because of the use of Gaussian Process as a proxy model for optimization, when there is a lot of data, the training of Gaussian Process will be very slow...

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

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...

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

different vein, the machine learning community has proposed the use of Gaussian process regression models to obtain a GARCH scheme. This results in a nonparametric...

theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional...

perform strictly better as layer width is increased. The Neural Network Gaussian Process (NNGP) corresponds to the infinite width limit of Bayesian neural networks...

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

regression method. A Gaussian process (GP) is a collection of random variables, any finite number of which have a joint Gaussian (normal) distribution...