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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 ...
Gaussian / ˈ ɡ aʊ s i ə n / is a general purpose computational chemistry software package initially released in 1970 by John Pople [1] [2] and his research group at Carnegie Mellon University as Gaussian 70. [3] It has been continuously updated since then. [4]
Quantum computational chemistry is an emerging field that exploits quantum computing to simulate chemical systems. Despite quantum mechanics' foundational role in understanding chemical behaviors, traditional computational approaches face significant challenges, largely due to the complexity and computational intensity of quantum mechanical equations.
Chemical accuracy is the accuracy required to make realistic chemical predictions and is generally considered to be 1 kcal/mol or 4 kJ/mol. To reach that accuracy in an economic way, it is necessary to use a series of post-Hartree–Fock methods and combine the results. These methods are called quantum chemistry composite methods. [56]
Gauss–Markov stochastic processes (named after Carl Friedrich Gauss and Andrey Markov) are stochastic processes that satisfy the requirements for both Gaussian processes and Markov processes. [1] [2] A stationary Gauss–Markov process is unique [citation needed] up to rescaling; such a process is also known as an Ornstein–Uhlenbeck process.
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]
Gaussian process approximations can often be expressed in terms of assumptions on under which and can be calculated with much lower complexity. Since these assumptions are generally not believed to reflect reality, the likelihood and the best predictor obtained in this way are not exact, but they are meant to be close to their original values.
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 .