State space models have gained tremendous popularity in recent years in as disparate
fields as engineering, economics, genetics and ecology. After a detailed introduction to …

Standard regression approaches assume that some finite number of the response
distribution characteristics, such as location and scale, change as a (parametric or …

A novel data‐driven adaptive robust optimization framework that leverages big data in
process industries is proposed. A Bayesian nonparametric model—the Dirichlet process …

We propose a class of kernel stick-breaking processes for uncountable collections of
dependent random probability measures. The process is constructed by first introducing an …

Many complex dynamical phenomena can be effectively modeled by a system that switches
among a set of conditionally linear dynamical modes. We consider two such models: the …

Many nonlinear dynamical phenomena can be effectively modeled by a system that
switches among a set of conditionally linear dynamical modes. We consider two such …

We describe a novel class of Bayesian nonparametric priors based on stick-breaking
constructions where the weights of the process are constructed as probit transformations of …

This paper presents a latent variable representation of regularized support vector machines
(SVM's) that enables EM, ECME or MCMC algorithms to provide parameter estimates. We …

Bayesian (machine) learning has been playing a significant role in machine learning for a
long time due to its particular ability to embrace uncertainty, encode prior knowledge, and …

The nonlinear, history-dependent macroscopic behavior of a granular material is rooted in
the micromechanics between constituent particles and irreversible, plastic deformations …