Model search in probit regression is often conducted by simultaneously exploring the model and parameter space, using a reversible jump MCMC sampler. Standard samplers often have low model acceptance ...

This paper offers a Bayesian framework for the calibration of financial models using neural stochastic differential equations ...

Although it is common practice to fit a complex Bayesian model using Markov chain Monte Carlo (MCMC) methods, we provide an alternative sampling-based method to fit a two-stage hierarchical model in ...

An academia-industry collaboration developed a new sampling algorithm for Design of Experiment intending to democratize experimental design.

Gamma-ray flares from blazars can be accompanied by high-energy neutrino emission. To better understand this phenomenon, an international research team has statistically analyzed 145 bright blazars.

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...