Adaptive Scan Gibbs Sampler for Large Scale Inference Problems
Smolyakov, Vadim, Qiang Liu, John W. Fisher · arXiv (Cornell University) · 2018
For large scale on-line inference problems the update strategy is critical for performance. We derive an adaptive scan Gibbs sampler that optimizes the update frequency by selecting an optimum mini-batch size. We demonstrate performance of our adaptive batch-size Gibbs sampler by comparing it against the collapsed Gibbs sampler for Bayesian Lasso, Dirichlet Process Mixture Models (DPMM) and Latent Dirichlet Allocation (LDA) graphical models.