Exact slice sampler for Hierarchical Dirichlet Processes
This provides a more efficient and parallelizable sampling method for researchers and practitioners using HDP models, though it is incremental as it builds on existing slice sampling ideas.
The authors tackled the problem of sampling from Hierarchical Dirichlet Processes (HDPs) by proposing an exact slice sampler, which achieves fast mixing and allows natural truncation of infinite measures without ad-hoc modifications.
We propose an exact slice sampler for Hierarchical Dirichlet process (HDP) and its associated mixture models (Teh et al., 2006). Although there are existing MCMC algorithms for sampling from the HDP, a slice sampler has been missing from the literature. Slice sampling is well-known for its desirable properties including its fast mixing and its natural potential for parallelization. On the other hand, the hierarchical nature of HDPs poses challenges to adopting a full-fledged slice sampler that automatically truncates all the infinite measures involved without ad-hoc modifications. In this work, we adopt the powerful idea of Bayesian variable augmentation to address this challenge. By introducing new latent variables, we obtain a full factorization of the joint distribution that is suitable for slice sampling. Our algorithm has several appealing features such as (1) fast mixing; (2) remaining exact while allowing natural truncation of the underlying infinite-dimensional measures, as in (Kalli et al., 2011), resulting in updates of only a finite number of necessary atoms and weights in each iteration; and (3) being naturally suited to parallel implementations. The underlying principle for joint factorization of the full likelihood is simple and can be applied to many other settings, such as designing sampling algorithms for general dependent Dirichlet process (DDP) models.