  A mixture of experts models the conditional density of a response variable
using a mixture of regression models with covariate-dependent mixture weights.
We extend the finite mixture of experts model by allowing the parameters in
both the mixture components and the weights to evolve in time by 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 time-varying parameters,
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.
