Gaussian Processes For Machine Learning

@article{Seeger2004GaussianPF,
  title={Gaussian Processes For Machine Learning},
  author={Matthias W. Seeger},
  journal={International journal of neural systems},
  year={2004},
  volume={14 2},
  pages={69-106}
}
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches… CONTINUE READING
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