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Issues in Bayesian Analysis of Neural Network Models

Müller1, Insua

  • 1Duke University, Institute of Statistics and Decision Sciences, Durham NC, US, Box 90251, 27708. pm@isds.duke.edu

Neural Computation
|April 4, 1998
PubMed
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This study introduces an efficient Markov chain Monte Carlo scheme for Bayesian neural network analysis. The method enables effective inference and prediction, and can automatically determine optimal network architectures from data.

Area of Science:

  • Machine Learning
  • Computational Statistics
  • Artificial Intelligence

Background:

  • Bayesian analysis of neural networks is gaining traction, building on foundational work.
  • The computational complexity of Bayesian neural networks presents a significant challenge.

Purpose of the Study:

  • To develop an efficient computational scheme for Bayesian inference and prediction in neural networks.
  • To extend this scheme for data-driven determination of neural network architectures.

Main Methods:

  • A novel, efficient Markov chain Monte Carlo (MCMC) scheme is proposed for fixed-architecture feedforward neural networks.
  • The MCMC scheme is extended to handle variable network architectures.

Main Results:

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  • The proposed MCMC scheme significantly improves computational efficiency for Bayesian neural network inference.
  • The extended scheme offers a data-driven approach to identify appropriate neural network architectures.

    • Conclusions:

    • The developed MCMC method provides a computationally feasible approach to Bayesian neural network analysis.
    • This work facilitates automated, data-driven model selection for neural networks.