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Bayesian models provide a framework for probabilistic modelling of complexdatasets. However, many of such models are computationally demanding especiallyin the presence of large datasets. On the other hand, in sensor networkapplications, statistical (Bayesian) parameter estimation usually needsdistributed algorithms, in which both data and computation are distributedacross the nodes of the network. In this paper we propose a general frameworkfor distributed Bayesian learning using Bregman Alternating Direction Method ofMultipliers (B-ADMM). We demonstrate the utility of our framework, with MeanField Variational Bayes (MFVB) as the primitive for distributed MatrixFactorization (MF) and distributed affine structure from motion (SfM).

D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM