[2010.06768] Flexible mean field variational inference using mixtures of non-overlapping exponential familiesopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

Sparse models are desirable for many applications across diverse domains as they can perform automatic variable selection, aid interpretability, and provide regularization. When fitting sparse models in a Bayesian framework, however, analytically obtaining a posterior distribution over the parameters of interest is intractable for all but the simplest cases. As a result practitioners must rely on either sampling algorithms such as Markov chain Monte Carlo or variational methods to obtain an approximate posterior. Mean field variational inference is a particularly simple and popular framework that is often amenable to analytically deriving closed-form parameter updates. When all distributions in the model are members of exponential families and are conditionally conjugate, optimization schemes can often be derived by hand. Yet, I show that using standard mean field variational inference can fail to produce sensible results for models with sparsity-inducing priors, such as the spike-and-

3 mentions: @spence_jeffrey_
Date: 2020/10/16 15:52

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@spence_jeffrey_ I'm happy to say that I've finally gotten my NeurIPS submission preprinted. Short thread below! 1/6 t.co/9vF43d14sN

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