Sequential Monte Carlo samplers for Dirichlet Process Mixtures

In this paper, we develop a novel online algorithm based on the Sequential Monte Carlo (SMC) samplers framework for posterior inference in Dirichlet Process Mixtures (DPM) (DelMoral et al., 2006). Our method generalizes many sequential importance sampling approaches. It provides a computationally efficient improvement to particle filtering that is less prone to getting stuck in isolated modes. The proposed method is a particular SMC sampler that enables us to design sophisticated clustering update schemes, such as updating past trajectories of the particles in light of recent observations, and still ensures convergence to the true DPM tar-get distribution asymptotically. Performance has been evaluated in a Bayesian Infinite Gaussian mixture density estimation problem and it is shown that the proposed algorithm outperforms conventional Monte Carlo approaches in terms of estimation variance and average log-marginal likelihood.