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Scalable computations for nonstationary Gaussian processes

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Abstract

Nonstationary Gaussian process models can capture complex spatially varying dependence structures in spatial data. However, the large number of observations in modern datasets makes fitting such models computationally intractable with conventional dense linear algebra. In addition, derivative-free or even first-order optimization methods can be slow to converge when estimating many spatially varying parameters. We present here a computational framework that couples an algebraic block-diagonal plus low-rank covariance matrix approximation with stochastic trace estimation to facilitate the efficient use of second-order solvers for maximum likelihood estimation of Gaussian process models with many parameters. We demonstrate the effectiveness of these methods by simultaneously fitting 192 parameters in the popular nonstationary model of Paciorek and Schervish using 107,600 sea surface temperature anomaly measurements.

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Acknowledgements

This material was based upon work supported by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) under Contract DE-AC02-06CH11347. We would also like to thank the anonymous referees for many helpful comments which led to significant improvements to the manuscript.

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Correspondence to Paul G. Beckman.

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This material was based upon work supported by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) under Contract DE-AC02-06CH11347.

Appendix

Appendix

1.1 Computing the Hessian

To employ second-order Newton solvers instead of Fisher scoring to compute the MLE, one must compute the Hessian, whose entries are given by

$$\begin{aligned} \big [-\nabla ^2\ell (\varvec{\theta })\big ]_{jk}&= -{\mathscr {I}}_{jk} + \frac{1}{2}{{\,\textrm{tr}\,}}\Bigg [\tilde{\varvec{\Sigma }}^{-1} \bigg (\frac{\partial ^2\tilde{\varvec{\Sigma }}}{\partial \theta _j\partial \theta _k}\bigg ) \Bigg ]\nonumber \\&\quad +\varvec{y}^\top \tilde{\varvec{\Sigma }}^{-1} \bigg (\frac{\partial \tilde{\varvec{\Sigma }}}{\partial \theta _j}\bigg ) \tilde{\varvec{\Sigma }}^{-1} \bigg (\frac{\partial \tilde{\varvec{\Sigma }}}{\partial \theta _k}\bigg ) \tilde{\varvec{\Sigma }}^{-1} \varvec{y}\nonumber \\&\quad - \frac{1}{2}\varvec{y}^\top \tilde{\varvec{\Sigma }}^{-1} \bigg (\frac{\partial ^2\tilde{\varvec{\Sigma }}}{\partial \theta _j\partial \theta _k}\bigg ) \tilde{\varvec{\Sigma }}^{-1} \varvec{y}. \end{aligned}$$
(51)

We can again apply basic matrix differentiation rules to equation (19) to obtain the second derivatives of the Nyström approximation, where the second derivative of the rank-p Nyström approximation

$$\begin{aligned}&\frac{\partial ^2}{\partial \theta _j\partial \theta _k}\Big (\varvec{\Sigma }_{QP}\varvec{\Sigma }_{PP}^{-1}\varvec{\Sigma }_{QP}^\top \Big )\nonumber \\&\quad = \bigg (\frac{\partial ^2\varvec{\Sigma }_{QP}}{\partial \theta _j\partial \theta _k}\bigg )\varvec{\Sigma }_{PP}^{-1}\varvec{\Sigma }_{QP}^\top \nonumber \\&\qquad - \bigg (\frac{\partial \varvec{\Sigma }_{QP}}{\partial \theta _j}\bigg )\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{PP}}{\partial \theta _k}\bigg )\varvec{\Sigma }_{PP}^{-1}\varvec{\Sigma }_{QP}^\top \nonumber \\&\qquad + \bigg (\frac{\partial \varvec{\Sigma }_{QP}}{\partial \theta _j}\bigg )\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{QP}^\top }{\partial \theta _k}\bigg ) \nonumber \\&\qquad - \bigg (\frac{\partial \varvec{\Sigma }_{QP}}{\partial \theta _k}\bigg )\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{PP}}{\partial \theta _j}\bigg )\varvec{\Sigma }_{PP}^{-1}\varvec{\Sigma }_{QP}^\top \nonumber \\&\qquad + \varvec{\Sigma }_{QP}\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{PP}}{\partial \theta _k}\bigg )\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{PP}}{\partial \theta _j}\bigg )\varvec{\Sigma }_{PP}^{-1}\varvec{\Sigma }_{QP}^\top \nonumber \\&\qquad - \varvec{\Sigma }_{QP}\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial ^2\varvec{\Sigma }_{PP}}{\partial \theta _j\partial \theta _k}\bigg )\varvec{\Sigma }_{PP}^{-1}\varvec{\Sigma }_{QP}^\top \nonumber \\&\qquad + \varvec{\Sigma }_{QP}\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{PP}}{\partial \theta _j}\bigg )\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{PP}}{\partial \theta _k}\bigg )\varvec{\Sigma }_{PP}^{-1}\varvec{\Sigma }_{QP}^\top \nonumber \\&\qquad - \varvec{\Sigma }_{QP}\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{PP}}{\partial \theta _j}\bigg )\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{QP}^\top }{\partial \theta _k}\bigg ) \nonumber \\&\qquad + \bigg (\frac{\partial \varvec{\Sigma }_{QP}}{\partial \theta _k}\bigg )\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{QP}}{\partial \theta _j}\bigg )^\top \nonumber \\&\qquad - \varvec{\Sigma }_{QP}\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{PP}}{\partial \theta _k}\bigg )\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial \varvec{\Sigma }_{QP}}{\partial \theta _j}\bigg )^\top \nonumber \\&\qquad + \varvec{\Sigma }_{QP}\varvec{\Sigma }_{PP}^{-1}\bigg (\frac{\partial ^2\varvec{\Sigma }_{QP}}{\partial \theta _j\partial \theta _k}\bigg )^\top \end{aligned}$$
(52)

has rank at most 4p. The second derivative of the approximate covariance matrix \(\tilde{\varvec{\Sigma }}\) is then given by

$$\begin{aligned} \frac{\partial ^2\tilde{\varvec{\Sigma }}}{\partial \theta _j\partial \theta _k} = \varvec{\Pi }^\top \begin{bmatrix} \displaystyle \frac{\partial ^2}{\partial \theta _j\partial \theta _k}\Big (\varvec{\Sigma }_{QP}\varvec{\Sigma }_{PP}^{-1}\varvec{\Sigma }_{QP}^\top \Big ) + \frac{\partial ^2\varvec{D}}{\partial \theta _j\partial \theta _k} &{} \displaystyle \frac{\partial ^2\varvec{\Sigma }_{QP}}{\partial \theta _j\partial \theta _k} \\ \displaystyle \frac{\partial ^2\varvec{\Sigma }_{QP}^\top }{\partial \theta _j\partial \theta _k} &{} \displaystyle \frac{\partial ^2\varvec{\Sigma }_{PP}}{\partial \theta _j\partial \theta _k}\end{bmatrix} \varvec{\Pi }, \nonumber \\ \end{aligned}$$
(53)

which follows the same permuted block diagonal plus low-rank structure as \(\tilde{\varvec{\Sigma }}\) and \(\frac{\partial \tilde{\varvec{\Sigma }}}{\partial \theta _j}\), now with rank at most 4p in the low-rank portion of the upper left block. Thus the trace term in each Hessian entry can be computed using equation (21) exactly as was done for the analogous term in the gradient. This results in a linear complexity algorithm for computing entries of the Hessian.

1.2 Nonstationary parameters for simulated data

For our approximation accuracy study in Sect. 4.3, we generate data from the Paciorek-Schervish model (6) using a continuously varying local anisotropy matrix \(\varvec{\Lambda }(\varvec{x})\) given by

$$\begin{aligned} \varvec{\Lambda }(\varvec{x})&= \begin{bmatrix}\cos (\theta \varvec{x})) &{} -\sin (\theta (\varvec{x})) \\ \sin (\theta (\varvec{x})) &{} \cos (\theta (\varvec{x}))\end{bmatrix} \begin{bmatrix}\lambda _1(\varvec{x}) &{} \\ {} &{} \lambda _2(\varvec{x})\end{bmatrix}\nonumber \\&\quad \times \begin{bmatrix}\cos (\theta (\varvec{x})) &{} -\sin (\theta (\varvec{x})) \\ \sin (\theta (\varvec{x})) &{} \cos (\theta (\varvec{x}))\end{bmatrix}^T, \end{aligned}$$
(54)
$$\begin{aligned} \theta (\varvec{x})&= \text {acos}\left( \text {min}\left( 1,\frac{\varvec{x}^T \varvec{x}_{\theta }^*}{||\varvec{x}|| ||\varvec{x}_{\theta }^*||}\right) \right) , \end{aligned}$$
(55)
$$\begin{aligned} \lambda _1(\varvec{x})&= \exp (2\cos (4 ||\varvec{x} - \varvec{x}_{1}^*||) - 3), \quad \text {and} \end{aligned}$$
(56)
$$\begin{aligned} \lambda _2(\varvec{x})&= \exp (2\cos (4 ||\varvec{x} - \varvec{x}_{2}^*||) - 3), \end{aligned}$$
(57)

where \(\varvec{x}_{\theta }^* = [1.75, 2.25]\), \(\varvec{x}_{1}^* = [0.75, 0.5]\), and \(\varvec{x}_{2}^* = [0.3, 0.2]\). These functions and values were chosen in an ad hoc way simply to provide sample paths with interesting features that resemble real data.

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Beckman, P.G., Geoga, C.J., Stein, M.L. et al. Scalable computations for nonstationary Gaussian processes. Stat Comput 33, 84 (2023). https://doi.org/10.1007/s11222-023-10252-0

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