Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose novel inference method for DGPs computer model emulation. By stochastically imputing the latent layers, our approach transforms DGP into linked GP: emulator developed systems models. This transformation pe...

The goal of our research was to compare and systematize several approaches non-parametric null hypothesis significance testing using computer-based statistical modeling. For teaching purposes, a model for simulation created. results were analyzed Fisher's angular transformation, Chi-square, Mann-Whitney, exact tests. Appropriate software created, allowing us recommend new illustrative materials...