Dynamic Mixture of Experts Models for Online Prediction

A mixture of experts models the conditional density of a response variable using a finite mixture of regression models with covariate-dependent mixture weights. We extend the model by allowing the parameters in both the mixture components and the weights to evolve in time following random walk processes. Inference for time-varying parameters in richly parameterized mixture of experts models is challenging. We propose a sequential Monte Carlo algorithm for online inference and based on a tailored proposal distribution built on ideas from linear Bayes methods and the EM algorithm. The method gives a unified treatment for mixtures with essentially any density components, including the special case of static parameters. We assess the properties of the method on simulated data and on industrial data where the aim is to predict software faults in a continuously upgraded large-scale software project.