all AI news
Sample Path Regularity of Gaussian Processes from the Covariance Kernel
Feb. 19, 2024, 5:43 a.m. | Natha\"el Da Costa, Marvin Pf\"ortner, Lancelot Da Costa, Philipp Hennig
cs.LG updates on arXiv.org arxiv.org
Abstract: Gaussian processes (GPs) are the most common formalism for defining probability distributions over spaces of functions. While applications of GPs are myriad, a comprehensive understanding of GP sample paths, i.e. the function spaces over which they define a probability measure, is lacking. In practice, GPs are not constructed through a probability measure, but instead through a mean function and a covariance kernel. In this paper we provide necessary and sufficient conditions on the covariance kernel …
abstract applications arxiv covariance cs.lg function functions gaussian processes gps kernel math.pr math.st path practice probability processes sample spaces stat.ml stat.th type understanding
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Senior Software Engineer, Generative AI (C++)
@ SoundHound Inc. | Toronto, Canada