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

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 ...

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 ...

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 ...

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