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Integrated non-factorized variational inference

Publication ,  Journal Article
Han, S; Liao, X; Carin, L
Published in: Advances in Neural Information Processing Systems
January 1, 2013

We present a non-factorized variational method for full posterior inference in Bayesian hierarchical models, with the goal of capturing the posterior variable dependencies via efficient and possibly parallel computation. Our approach unifies the integrated nested Laplace approximation (INLA) under the variational framework. The proposed method is applicable in more challenging scenarios than typically assumed by INLA, such as Bayesian Lasso, which is characterized by the non-differentiability of the ℓ1 norm arising from independent Laplace priors. We derive an upper bound for the Kullback-Leibler divergence, which yields a fast closed-form solution via decoupled optimization. Our method is a reliable analytic alternative to Markov chain Monte Carlo (MCMC), and it results in a tighter evidence lower bound than that of mean-field variational Bayes (VB) method.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2013

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

APA
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ICMJE
MLA
NLM
Han, S., Liao, X., & Carin, L. (2013). Integrated non-factorized variational inference. Advances in Neural Information Processing Systems.
Han, S., X. Liao, and L. Carin. “Integrated non-factorized variational inference.” Advances in Neural Information Processing Systems, January 1, 2013.
Han S, Liao X, Carin L. Integrated non-factorized variational inference. Advances in Neural Information Processing Systems. 2013 Jan 1;
Han, S., et al. “Integrated non-factorized variational inference.” Advances in Neural Information Processing Systems, Jan. 2013.
Han S, Liao X, Carin L. Integrated non-factorized variational inference. Advances in Neural Information Processing Systems. 2013 Jan 1;

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2013

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology