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 ...

Bayesian graphical models provide a principled framework for representing complex dependency structures among multivariate variables by combining graph theory with probabilistic inference. In these ...

Scientists have confirmed that human brains are naturally wired to perform advanced calculations, much like a high-powered computer, to make sense of the world through a process known as Bayesian ...

A Bayesian particle Gibbs framework enables unbiased spike time inference with millisecond resolution and jointly estimates uncertainties in both spike timing and model parameters from fast calcium ...

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, ...

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 ...

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 ...

"In this universe effect follows cause. I've complained about it, but. . ." -- House (Laurie), pre-sponding to D. Bem "The more extraordinary the event, the greater the need for it to be supported by ...