Tightening bounds for variational inference by revisiting perturbation theory

DOI10.1088/1742-5468/ab43d3zbMath1459.62037arXiv1910.00069OpenAlexW3101436368MaRDI QIDQ5854101

Cheng Zhang, Robert Bamler, Stephan Mandt, Manfred Opper

Publication date: 16 March 2021

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.00069

Mathematics Subject Classification ID

Gaussian processes (60G15) Nonparametric estimation (62G05) Bayesian inference (62F15)

Cites Work

• Fixed-form variational posterior approximation through stochastic linear regression
• On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods
• An introduction to variational methods for graphical models
• Expectation propagation
• Minimax Approximation of Optical Profiles
• A Stochastic Approximation Method
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