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Parameter estimation in high dimensional Gaussian distributions by Aune, Erlend; Simpson, Daniel P. is a scholarly article available to read on EtoBox.

What is Parameter estimation in high dimensional Gaussian distributions about?

In order to compute the log-likelihood for high dimensional spatial Gaussian models, it is necessary to compute the determinant of the large, sparse, symmetric positive definite precision matrix, Q. Traditional methods for evaluating the log-likelihood for very large models may fail due to the massive memory requirements. We present a novel approach for evaluating such likelihoods when the matrix-vector product, Qv, is fast to compute. In this approach we utilise matrix functions, Krylov subspaces, and probing vectors to construct an iterative method for computing the log-likelihood.

Author

Aune, Erlend; Simpson, Daniel P.

Published

2011

Language

EN