2011/05/26 by Erlend Aune, Daniel Simpson, Aune, Erlend +1
Chemistry · Environmental Science · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Numerical Analysis (math.NA) #Soil Geostatistics and Mapping #Spectroscopy and Chemometric Analyses #Statistical and numerical algorithms
paper · pdf · doi:10.48550/arxiv.1105.5256
openalex publication_date 2011/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.