Nature

Random Fields and Gaussian Processes

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 ...
JSTOR Daily

Response Models for Mixed Binary and Quantitative Variables

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 ...
GitHub

An example of ConditionalGaussianDistribution (Python code)

There was an error while loading. Please reload this page. There are two examples of the conditional Gaussian distribution with Python (Jupyter Notebook) code ...
IEEE

On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series

Abstract: Estimation of the conditional independence graph (CIG) of high-dimensional multivariate Gaussian time series from multi-attribute data is considered. Existing methods for graph estimation ...
mccormick.northwestern.edu

ELEC_ENG 422: Random Processes in Communications and Control I

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

Zhou Fang

Filtering Problem,Particle Filter,Additional Calculations,Artificial Noise,Computation Time,Computational Efficiency,Computational Effort,Conditional Density ...
Oak Ridge National Lab

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

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