Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HLIBCov: Parallel Hierarchical Matrix Approximation of Large Covariance
  Matrices and Likelihoods with Applications in Parameter Identification   We provide more technical details about the HLIBCov package, which is using
parallel hierarchical ($\H$-) matrices to identify unknown parameters of the
covariance function (variance, smoothness, and covariance length). These
parameters are estimated by maximizing the joint Gaussian log-likelihood
function. The HLIBCov package approximates large dense inhomogeneous covariance
matrices with a log-linear computational cost and storage requirement. We
explain how to compute the Cholesky factorization, determinant, inverse and
quadratic form in the H-matrix format. To demonstrate the numerical
performance, we identify three unknown parameters in an example with 2,000,000
locations on a PC-desktop.

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The categories of this paper are: q-fin
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: cs
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The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: patt-sol