Bayesian statistics represents a powerful framework for data analysis that centres on Bayes’ theorem, enabling researchers to update existing beliefs with incoming evidence. By combining prior ...

The parametric bootstrap can be used for the efficient computation of Bayes posterior distributions. Importance sampling formulas take on an easy form relating to the deviance in exponential families ...

Scientists have confirmed that human brains are naturally wired to perform advanced calculations, much like a high-powered computer, to make sense of the world through a process known as Bayesian ...

This is a preview. Log in through your library . Abstract The integrals involved in Bayesian inference have long served as an impediment to the wider application of these techniques in the analysis of ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

CAMBRIDGE, MA -- Pollsters trying to predict presidential election results and physicists searching for distant exoplanets have at least one thing in common: They often use a tried-and-true scientific ...

This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely ...

Forbes contributors publish independent expert analyses and insights. I had an opportunity to talk with the founders of a company called PiLogic recently about their approach to solving certain ...