Nonstationary Gaussian process models can capture complex spatially varying dependence structures in spatial data. However, the large number of observations modern datasets makes fitting such computationally intractable with conventional dense linear algebra. In addition, derivative-free or even first-order optimization methods be slow to converge when estimating many parameters. We present her...

Interstellar dust corrupts nearly every stellar observation and accounting for it is crucial to measuring physical properties of stars. We model the distribution as a spatially varying latent field with Gaussian process (GP) develop likelihood inference method that scales millions astronomical observations. Modeling interstellar complicated by two factors. The first integrated data come from va...

Numerical solutions of stochastic problems involving random processes X(t), which constitutes infinite families variables, require to represent these by finite dimensional (FD) models Xd(t), i.e., deterministic functions time depending on numbers d variables. Most available FD match the mean, correlation, and other global properties X(t). They provide useful information a broad range problems, ...