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Inference of continuous values with a Gaussian process prior is known as Gaussian process regression, or kriging; extending Gaussian process regression to multiple target variables is known as cokriging. [26] Gaussian processes are thus useful as a powerful non-linear multivariate interpolation tool. Kriging is also used to extend Gaussian ...
In statistics, originally in geostatistics, kriging or Kriging (/ ˈ k r iː ɡ ɪ ŋ /), also known as Gaussian process regression, is a method of interpolation based on Gaussian process governed by prior covariances. Under suitable assumptions of the prior, kriging gives the best linear unbiased prediction (BLUP) at unsampled locations. [1]
A non-trivial way to mix the latent functions is by convolving a base process with a smoothing kernel. If the base process is a Gaussian process, the convolved process is Gaussian as well. We can therefore exploit convolutions to construct covariance functions. [20] This method of producing non-separable kernels is known as process convolution.
This is a comparison of statistical analysis software that allows doing inference with Gaussian processes often using approximations.. This article is written from the point of view of Bayesian statistics, which may use a terminology different from the one commonly used in kriging.
The second is based on quantile regression using values of the process which are close to the value one is trying to predict, where distance is measured in terms of a metric on the set of indices. Local Approximate Gaussian Process uses a similar logic but constructs a valid stochastic process based on these neighboring values.
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 .
This method uses Gaussian process regression (GPR) to fit a probabilistic model from which replicates may then be drawn. GPR is a Bayesian non-linear regression method. A Gaussian process (GP) is a collection of random variables, any finite number of which have a joint Gaussian (normal) distribution.
It is a generalization of the logistic function to multiple dimensions, and is used in multinomial logistic regression. The softmax function is often used as the last activation function of a neural network to normalize the output of a network to a probability distribution over predicted output classes.