Abstract: Conventional neural network-based machine learning algorithms often encounter difficulties in data-limited scenarios or where interpretability is critical. Conversely, Bayesian ...

This repository includes theoretical notes, slides, and hands-on R examples for exploring Bayesian Linear Regression. It introduces both classical and Bayesian regression methods, showing how to ...

Physicists have derived a quantum version of Bayes’ rule, revealing how beliefs and probabilities update in the quantum realm with potential applications in computing and beyond. Credit: Shutterstock ...

I have a real world project in the following scenario: I have a predefined network skeleton in DAG format. Our dataset has 1654 nodes, 2965 edges. I also have a dataset with shape (3000, 1654). Among ...

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 ...

Objectives: We aimed to clarify the influence of facial expressions on providing early recognition and diagnosis of Parkinson’s disease (PD). Methods: We included 18 people with PD and 18 controls.

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, ...