The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this overconfidence are unknown. In ...

Selecting a subset of variables for linear models remains an active area of research. This article reviews many of the recent contributions to the Bayesian model selection and shrinkage prior ...

Quantitative rating systems are increasingly being used for the purposes of capital allocation and pricing credits. For these purposes, it is important to validate the accuracy of the probability of ...

What Oncologists Want: Identifying Challenges and Preferences on Diagnosis Data Entry to Reduce EHR-Induced Burden and Improve Clinical Data Quality We trained a Bayesian ML model in 10,318 patients ...

Operational risk is hard to quantify, due to the presence of heavy tailed loss distributions. Extreme value distributions, used in this context, are very sensitive to the data, and this is a problem ...

Some of you may have come across a growing number of publications in your field using an alternative paradigm called Bayesian statistics in which to perform their statistical analyses. The goal of ...

This post originally appeared on Allen Downey’s personal blog. Last fall I taught an introduction to Bayesian statistics at Olin College. My students worked on some excellent projects, and I invited ...