A Bayesian re-analysis of hundreds of published clinical trials shows that statistically negative studies can hide strong ...

Among the books in my 'to-read' pile that I don't remember when I added, there was a book called 'The Grand Unified Theory of ...

How does one model a simple cell-signaling pathway? Consider a simple example consisting of a stimulant, an extracellular signal, an inhibitor of the signal, a G protein–coupled receptor, a G protein ...

In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...

Many would probably be content to use Bayesian methodology for hypothesis testing, if it was easy, objective and with trustworthy assumptions. The Bayesian information criterion and some simple bounds ...

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 ...

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 ...

Nate Silver, baseball statistician turned political analyst, gained a lot of attention during the 2012 United States elections when he successfully predicted the outcome of the presidential vote in ...

Researchers have fused FMEA, Bayesian networks, risk matrices, and fuzzy evaluation into a single validated framework that cut the failure risk of drilling permanent magnet synchronous motors and ...