Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

We consider the asymptotic behaviour of the marginal maximum likelihood empirical Bayes posterior distribution in general setting. First, we characterize the set where the maximum marginal likelihood ...

It is proved in the paper that every possible set of conditions which implies consistency of the maximum likelihood method also implies consistency of Bayes estimates for a large class of prior ...

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 ...

under H 0 and H 1, respectively, where n represents the total sample size in the study (cases and controls). M 0 (n,k) is analogous to a type I error, yet is not fixed by design at α. M i (n,k) i=0,1 ...

Whether in everyday life or in the lab, we often want to make inferences about hypotheses. Whether I’m deciding it’s safe to run a yellow light, when I need to leave home in order to make it to my ...

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American I’m not sure when I first heard of Bayes’ ...