Local, algebraic simplifications of Gaussian random fields

• Published in: arXiv.org
• Main Authors: Bjorkmo, Theodor, Marsh, M C David
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 08.05.2018
• Subjects: Covariance matrix; Fields (mathematics); Gaussian process; Machine learning; Mathematical analysis; Matrix methods; Maxima; Potential energy; Probability density functions; Random processes; Statistical analysis; Taylor series; Universe

Many applications of Gaussian random fields and Gaussian random processes are limited by the computational complexity of evaluating the probability density function, which involves inverting the relevant covariance matrix. In this work, we show how that problem can be completely circumvented for the...