Affine Independent Variational Inference

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Edward Challis, David Barber


We present a method for approximate inference for a broad class of non-conjugate probabilistic models. In particular, for the family of generalized linear model target densities we describe a rich class of variational approximating densities which can be best fit to the target by minimizing the Kullback-Leibler divergence. Our approach is based on using the Fourier representation which we show results in efficient and scalable inference.