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 ...

In the Bayesian paradigm, a common method for comparing two models is to compute the Bayes factor, defined as the ratio of their respective marginal likelihoods. In recent phylogenetic works, the ...

Bayes’s core contribution, which Chivers skillfully renders into cogent prose designed to educate the lay reader, is the notion that the likelihood of an event taking place in the future depends, in ...

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 ...

I am putting myself to the fullest possible use, which is all I think that any conscious entity can ever hope to do. ~ Hal The Bayesians want us to be Bayesians (e.g, Krueger, 2017). This is just as ...

The stock market is an ever-changing place. In fact, it’s changing every second of every day as prices go up and down, and new factors impact the trajectory of the market. It’s important for investors ...