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 ...

We develop a Bayesian method for nonparametric model—based quantile regression. The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the ...

In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

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 ...

The statistical dispute was mentioned in the New York Times. The writer noted correctly that the fight over how to analyze data in the social sciences has been going on for decades. The Bem-vs.-Bayes ...

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 Western Conference standings are a jumbled mess with only five games separating positions No. 4 through No. 10. In the Eastern Conference, things are not as competitive, except at the top where ...