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Extended Variational Message Passing for Automated Approximate Bayesian Inference.

Semih Akbayrak1, Ivan Bocharov1, Bert de Vries1,2

  • 1Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600MB Eindhoven, The Netherlands.

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Summary
This summary is machine-generated.

Extended Variational Message Passing (EVMP) enhances Bayesian inference for complex models by approximating expectations. This approach makes automated inference more accessible for a wider range of probabilistic models.

Keywords:
Bayesian inferencefactor graphsprobabilistic programmingvariational inferencevariational message passing

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Statistics

Background:

  • Bayesian inference is crucial for data processing and is often formulated using generative probabilistic models.
  • Variational Message Passing (VMP) offers an automated framework for Bayesian inference in specific models.
  • Complex models with non-conjugate factors or nonlinear mappings challenge standard VMP.

Purpose of the Study:

  • To extend the applicability of Variational Message Passing (VMP) to more complex probabilistic models.
  • To develop an automated and efficient inference engine for a broader class of factorized probabilistic models.
  • To address limitations of VMP in handling deterministic mappings and non-conjugate distributions.

Main Methods:

  • Introduced Extended Variational Message Passing (EVMP) by approximating expectations of hidden variables.
  • Utilized importance sampling and Laplace approximation for expectation approximation.
  • Implemented EVMP within the ForneyLab.jl probabilistic programming package in Julia.

Main Results:

  • EVMP successfully handles complex models previously intractable for standard VMP.
  • The approach supports automated and efficient Bayesian inference across diverse model specifications.
  • Demonstrated EVMP's capability as a versatile inference engine through various examples.

Conclusions:

  • EVMP significantly broadens the scope of automated Bayesian inference.
  • The method provides a robust solution for inference in complex factorized probabilistic models.
  • EVMP represents a near-universal inference engine for factorized probabilistic models.