Latest Articles for Covariance

A novel approach enhances accuracy in predicting Hamiltonians for materials.

An overview of teleparallel gravity and its geometric approach to understanding gravitational effects.

New method improves detection of genetic influences on gene expression.

A new method improves outlier detection and estimation in multi-dimensional datasets.

A look at how galaxies cluster using advanced statistical models.

This article discusses methods for modeling and identifying switched systems under uncertainty.

Explore the role of random tensors in analyzing complex, multi-dimensional data.

Explore how uncertainty affects the behavior of dynamical systems over time.

A new method uses neural networks to enhance galaxy power spectrum data analysis.

Study examines methods to test changes in factor models using eigenvalues and eigenvectors.

This article explores prediction accuracy in zero-sum games and player behavior.

A new method enhances covariance estimation in Gaussian processes with adaptive thresholding.

Researchers introduce POP spectra to study parity violations in the Universe's structure.

This research aims to clarify the mechanics of GCP and Riemannian metrics.

A look at how transformations impact mean and covariance in data analysis.

WENDY algorithm offers insights into gene interactions through limited data.

Learn how Gaussian Processes improve predictions and manage uncertainty in data analysis.

Researchers use advanced models to refine our understanding of the Universe's first stars.

Exploring how stock price correlation varies throughout the trading day.

Learn how Gaussian processes address prediction uncertainty in machine learning.

Explore the role of eigenvalues in improving statistical analyses and model evaluations.

New method enhances Gaussian process models for faster predictions with real-time data.

This article examines new methods for assessing Gaussian random vectors' concentration around average values.

Exploring new methods for analyzing random structures in statistics.

New technique improves bispectrum estimation in cosmological studies.

An analysis of signal detection effectiveness through noise covariance estimation.

FVNNs provide a fair approach to data analysis and predictions.

A new method streamlines calculations for random effects in Generalized Linear Mixed Models.

A look into how CNNs learn image features and their universal similarities.

STVNN offers a robust solution for analyzing complex data over time.

Learn how forecast reconciliation enhances prediction accuracy across various data sources.

Learn how models retain knowledge while adapting to new examples.

A tool to check the suitability of Laplace approximation for statistical models.

Exploring how random fields model unpredictable systems in nature and finance.

A fresh approach to federated learning that balances privacy and efficiency.

Learn how IOX models analyze complex spatial data relationships.

A fresh approach improves insights from complex, interdependent datasets.