Abstract: In this paper, we extend Gaussian graphical models to proper quaternion Gaussian distributions. The properness assumption reduces the number of unknowns by a factor of four while graphical ...

This important study investigates frequency-dependent effects of transcutaneous tibial nerve stimulation (TTNS) on bladder function in healthy humans and, through a computational model, shows that low ...

Time series can be used to derive models to predict future values based ... offering insights for enhancing coral resilience. Gaussian Graphical Optimal Transport (GGOT) captures network ...

Abstract: This paper focuses on estimated Gaussian Graphical Models (GGM) from sets of experimental data. Some extension of known Bayesian methods are proposed, allowing to introduce score functions ...

Ren, M., Zhen, Y., & Wang, J. (2024). Transfer learning for tensor Gaussian graphical models. Journal of Machine Learning Research, 25(396), 1-40. function.R: This ...

Tetrachoric, Intraclass correlation coefficients, Bootstrap agreement, Rater reliability, Generalizability Theory, Analytic Hierarchy Process, and allows users to produce Gaussian Graphical Model and ...

Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of ...

A team led by Kenneth Merz, Ph.D., Staff at Cleveland Clinic's Center for Computational Life Sciences, showed how quantum ...

This is a potentially valuable modeling study on sequence generation in the hippocampus in a variety of behavioral contexts. While the scope of the model is ambitious, its presentation is incomplete ...