A number of special representations are considered for the joint distribution of qualitative, mostly binary, and quantitative variables. In addition to the conditional Gaussian models and to ...

Random fields and Gaussian processes constitute fundamental frameworks in modern probability theory and spatial statistics, providing robust tools for modelling complex dependencies over space and ...

CATALOG DESCRIPTION: Fundamentals of random variables; mean-squared estimation; limit theorems and convergence; definition of random processes; autocorrelation and stationarity; Gaussian and Poisson ...

This repository contains MATLAB scripts and a report for simulating and analyzing Gaussian random variables, focusing on their statistical properties such as mean, variance, and probability density ...

Welcome! This is a deep-dive into one of the most elegant tools in machine learning — Gaussian Processes (GPs). Over the past week, I’ve spent nearly 2 hours a day immersing myself in Gaussian ...

Abstract: Short-term forecasting is a ubiquitous practice in a wide range of energy systems, including forecasting demand, renewable generation, and electricity pricing. Although it is known that ...

We discuss properties of distributions that are multivariate totally positive of order two (MTP2) related to conditional independence. In particular, we show that any independence model generated by ...

Integrating monitoring data to efficiently update reservoir pressure and CO2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse ...