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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 not correspond to any actual feature, but which retain its key properties while simplifying calculations. Many of these approximation methods can be expressed in purely linear algebraic or functional analytic terms as matrix or function approximations. Others are purely algorithmic and cannot easily be rephrased as a modification of a statistical model.
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machine learning, Gaussianprocessapproximation is a computational method that accelerates inference tasks in the context of a Gaussianprocess model, most...
In probability theory and statistics, a Gaussianprocess is a stochastic process (a collection of random variables indexed by time or space), such that...
signal processing, mainly in digital signal processing, a Gaussian filter is a filter whose impulse response is a Gaussian function (or an approximation to...
to use Gaussianprocesses in high-dimensional settings. It has since been extensively generalized giving rise to many contemporary approximations. A joint...
In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued...
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 (a,f(a))} . For this reason, this process is also called the tangent line approximation. Linear approximations in this case are further improved when...
theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional...
§ Blackman window. The Fourier transform of a Gaussian is also a Gaussian. Since the support of a Gaussian function extends to infinity, it must either...
imaging science, difference of Gaussians (DoG) is a feature enhancement algorithm that involves the subtraction of one Gaussian blurred version of an original...
Kriging, (/ˈkriːɡɪŋ/) also known as Gaussianprocess regression, is a method of interpolation based on Gaussianprocess governed by prior covariances. Under...
of the intensity measure is a Gaussian random field, then the resulting process is known as a log Gaussian Cox process. More generally, the intensity...
the Gaussian scale space, where the image data in N dimensions is subjected to smoothing by Gaussian convolution. Most of the theory for Gaussian scale...
system of equations A x = b {\displaystyle A\mathbf {x} =\mathbf {b} } by Gaussian elimination). Iterative methods are often the only choice for nonlinear...
Markov processes, Lévy processes, Gaussianprocesses, random fields, renewal processes, and branching processes. The study of stochastic processes uses...
that perform better than Laplacian operator or its difference-of-Gaussiansapproximation for image-based matching using local SIFT-like image descriptors...
For the Gaussian ensembles, the limit of Ξ ( λ 0 ) {\displaystyle \Xi (\lambda _{0})} is known; thus, for GUE it is a determinantal point process with the...
constant. Gaussianprocess 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 Gaussianprocess B H ( t ) {\textstyle B_{H}(t)} on [ 0 , T ] {\textstyle [0,T]} , that...
filters tends towards a Gaussian as the order of the filter is increased. Compared to finite-order approximations of the Gaussian filter, the Bessel filter...
exponentially modified Gaussian distribution, a convolution of a normal distribution with an exponential distribution, and the Gaussian minus exponential distribution...
exponential terms, but it can be approximated by the first derivative of a Gaussian. Among the edge detection methods developed so far, Canny edge detection...
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