Bayes' Theorem: What It Is, Formula, and Examples

Bayes' theorem is a statistical formula used to calculate conditional probability. Learn how it works, how to calculate it ...
IEEE

Variational Bayesian inference based 2D-DOAs estimation for time-varying number of dynamic sources

Abstract: This paper proposes a variational Bayesian inference (VBI) based algorithm for gridless and online estimation of multiple two-dimensional directions of arrival (2D-DOAs), whose number and ...
GitHub

causal-inference-k2-algorithm

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 ...
eLife

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

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 ...
Frontiers

Structured noise champagne: an empirical Bayesian algorithm for electromagnetic brain imaging with structured noise

Introduction: Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG), magnetoencephalography (MEG), and also from invasive ...
JSTOR Daily

Learning from a lot: Empirical Bayes for high-dimensional model-based prediction

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, ...
IEEE

A Variational Bayesian Inference Theory of Elasticity and Its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimension

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) ...