Bayesian network structure learning using hybrid K2 search and hill climbing optimization. Discovers causal relationships in observational data across datasets with 8-50 variables and up to 10K ...
This paper presents a valuable software package, named "Virtual Brain Inference" (VBI), that enables faster and more efficient inference of parameters in dynamical system models of whole-brain ...
Dormancy is a widespread bet-hedging strategy across taxa, enabling organisms to survive natural and anthropogenic disturbances. It fundamentally alters eco-evolutionary processes, including ...
Stable distributions are well-known for their desirable properties and can effectively fit data with heavy tail. However, due to the lack of an explicit probability density function and finite second ...
Abstract: Although Bayesian interaction primitives exhibit strong capabilities in skill learning and reproduction for physical human–robot interactions, they require extensive demonstrations and fail ...
Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...
Abstract: In this work, we have developed a variational Bayesian inference theory of elasticity, which is accomplished by using a mixed Variational Bayesian inference Finite Element Method (VBI-FEM) ...
The mathematics that enable sensor fusion include probabilistic modeling and statistical estimation using Bayesian inference and techniques like particle filters, Kalman filters, and α-β-γ filters, ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results