Adaptive Scan Gibbs Sampler for Large Scale Inference Problems
Machine Learning
2018-01-30 v1
Abstract
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.
Cite
@article{arxiv.1801.09144,
title = {Adaptive Scan Gibbs Sampler for Large Scale Inference Problems},
author = {Vadim Smolyakov and Qiang Liu and John W. Fisher},
journal= {arXiv preprint arXiv:1801.09144},
year = {2018}
}