Nature

Bayesian Methods in Structural Equation Modeling

Bayesian methods in Structural Equation Modeling (SEM) represent a paradigm shift in statistical analysis, integrating prior beliefs with empirical data to derive robust parameter estimates. This ...
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, ...
Diginomica

Can Bayesian Networks provide answers when Machine Learning comes up short? It's a question of probabilities

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...
JSTOR Daily

Adaptation of Bayesian Data Mining Algorithms to Longitudinal Claims Data: Coxib Safety as an Example

Introduction: Bayesian data mining methods have been used to evaluate drug safety signals from adverse event reporting systems and allow for evaluation of multiple endpoints that are not prespecified.
Investopedia

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

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...