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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Published on: October 27, 2016

Divergence measures and a general framework for local variational approximation.

Kazuho Watanabe¹, Masato Okada, Kazushi Ikeda

¹Graduate School of Information Science, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Nara, Japan. wkazuho@is.naist.jp

Neural Networks : the Official Journal of the International Neural Network Society

|July 2, 2011

Summary

This summary is machine-generated.

This study introduces a general framework for local variational approximation in Bayesian learning. It simplifies complex posterior distributions, offering an efficient method for marginal likelihood estimation.

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Area of Science:

Machine Learning
Bayesian Inference
Computational Statistics

Background:

Approximating intractable posterior distributions is crucial in Bayesian learning.
Existing methods often face computational challenges with complex models.
Variational methods offer a promising alternative for distribution approximation.

Purpose of the Study:

To formulate a general framework for local variational approximation.
To analyze the objective function of local variational approximation.
To develop an efficient method for marginal likelihood upper bound evaluation.

Main Methods:

Formulation of a general local variational approximation framework.
Decomposition of the objective function into Kullback information and Bregman divergence.
Geometrical arguments in the space of approximating posteriors.
Development of an efficient marginal likelihood upper bound evaluation method.

Main Results:

The objective function is shown to be decomposable.
An efficient method for evaluating an upper bound of the marginal likelihood is proposed.
The variational Bayesian approach for latent variable models is identified as a special case.

Conclusions:

The proposed general framework provides a unified view of local variational approximation.
The efficient marginal likelihood evaluation method enhances practical applicability.
This work advances the understanding and application of variational methods in Bayesian learning.