D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM
This work addresses the problem of scalable Bayesian inference for researchers and practitioners in fields like sensor networks, though it appears incremental as it adapts existing methods to a distributed setting.
The paper tackles the computational challenge of Bayesian models with large datasets by proposing D-MFVI, a distributed framework using Bregman ADMM for Bayesian learning, and demonstrates its utility in distributed matrix factorization and structure from motion applications.
Bayesian models provide a framework for probabilistic modelling of complex datasets. However, many of such models are computationally demanding especially in the presence of large datasets. On the other hand, in sensor network applications, statistical (Bayesian) parameter estimation usually needs distributed algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a general framework for distributed Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM). We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed Matrix Factorization (MF) and distributed affine structure from motion (SfM).