Bayesian methods have emerged as a pivotal framework in the design and analysis of clinical trials, offering a systematic approach for updating evidence as new data become available. By utilising ...

Bayesian inference has been used for genetic risk calculation. In this traditional method, inheritance events are divided into a number of cases under the inheritance model, and some elements of the ...

Researchers have published an article arguing that Bayesian methodology, a statistical tool introduced by Rev. Thomas Bayes in the 18th Century, is vital in providing solutions to many difficult ...

The application of Bayesian methods to large-scale phylogenetics problems is increasingly limited by computational issues, motivating the development of methods that can complement existing Markov ...

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, ...

A research team has developed a new technique to rapidly and accurately determine the charge state of electrons confined in semiconductor quantum dots -- fundamental components of quantum computing ...

Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent ...

This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited ...