Gaussian processes are arguably the most important class of spatiotemporal models within
machine learning. They encode prior information about the modeled function and can be …

Gaussian processes are used in many machine learning applications that rely on
uncertainty quantification. Recently, computational tools for working with these models in …

The advent of data science has provided an increasing number of challenges with high data
complexity. This paper addresses the challenge of space-time data where the spatial …

Gaussian process regression is widely used because of its ability to provide well-calibrated
uncertainty estimates and handle small or sparse datasets. However, it struggles with high …

Kernels are a fundamental technical primitive in machine learning. In recent years, kernel-
based methods such as Gaussian processes are becoming increasingly important in …

This paper considers generalised network, intended as networks where (a) the edges
connecting the nodes are nonlinear, and (b) stochastic processes are continuously indexed …

ЕРЕМЕЕВ Дмитрий Андреевич Выпускная квалификационная работа Моделир Page 1
Санкт–Петербургский государственный университет Факультет математики и компьютерных …