Scalable Inference in Dynamic Mixture Models


Previous work on inference for dynamic mixture models has so far been directed to models that follow a simple Brownian motion diffusion over time and pursued a batch inference approach. We generalize the underlying dynamics model to follow a Gaussian process, introducing a novel class of dynamic priors for mixture models. Further, we propose a stochastic variational inference scheme and compare our approach to previous solutions in terms of runtime complexity and test error.

NIPS 2016 Workshop on Advances in Approximate Bayesian Inference

More Publications