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#### Abstract

The choice of a function to approximate any given data, is not unique; such function can be found through different methods, in which the parameters of the model are calculated. This tool illustrates the process of sampling from a Gaussian process, to obtain a random function from a process with a given covariance and a mean of zero. Although the results are distributed around zero, this does not imply a loss of generality, since the mean can be changed by adding a function.

The Gaussian process tool takes a set of hyper parameters for a particular covariance function, which is used to calculate a covariance matrix. This matrix is positive definite and Cholesky decomposition can be used to make the sampling process computationally efficient.

#### References

- Rasmussen, C. E. and C. K. I. Williams (2006).
__Gaussian processes for machine learning__. Cambridge, Mass., MIT Press. - Langtangen, C.E. (2009).
__A primer on scientific programming with Python__. Springer.

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