Nature

Bayesian Spatial Statistics and Modeling

Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...
Scientific Research Publishing

KAN-Transformer Fusion with Mixture of Experts for Temporal Imputation of Spatiotemporal Air Pollution Data ()

The Kolmogorov-Arnold Network (abbr. KAN) is a novel neural network architecture inspired by the Kolmogorov-Arnold ...
Scientific Research Publishing

A Spatio-Temporal Prediction Model for Wall Turbulence Based on Hybrid Neural Networks ()

The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and ...
National Academies of Sciences%2c Engineering%2c and Medicine

Spatial Statistics and Digital Image Analysis

Unfortunately, this book can't be printed from the OpenBook. If you need to print pages from this book, we recommend downloading it as a PDF. Visit NAP.edu/10766 to get more information about this ...
Business Wire

New Esri Book Provides Accessible Understanding of the Most Widely Used ArcGIS Spatial Statistics Tools

REDLANDS, Calif.--(BUSINESS WIRE)--Thanks to the abundance of information and computational power now available to us, data science appears in every aspect of our lives. But with so many algorithms ...
Frontiers

Advancing High-Resolution 3T MRI for Cognitive and Clinical Neuroscience

Magnetic resonance imaging (MRI) at 3T is a cornerstone for neuroscientific research due to its widespread availability and versatility. The advent of ultra-high field (≥7T) scanners has significantly ...