ML Anthology

Issues in Bayesian Analysis of Neural Network Models

Peter Müller, David Ríos Insua
NeCo 1998 pp. 749-770
doi:10.1162/089976698300017737 /neco/1998/muller1998neco-issues/

Abstract

Stemming from work by Buntine and Weigend (1991) and MacKay (1992), there is a growing interest in Bayesian analysis of neural network models. Although conceptually simple, this problem is computationally involved. We suggest a very efficient Markov chain Monte Carlo scheme for inference and prediction with fixed-architecture feedforward neural networks. The scheme is then extended to the variable architecture case, providing a data-driven procedure to identify sensible architectures.

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Cite

Text

Müller and Insua. "Issues in Bayesian Analysis of Neural Network Models." Neural Computation, 1998. doi:10.1162/089976698300017737

Markdown

[Müller and Insua. "Issues in Bayesian Analysis of Neural Network Models." Neural Computation, 1998.](https://mlanthology.org/neco/1998/muller1998neco-issues/) doi:10.1162/089976698300017737

BibTeX

@article{muller1998neco-issues,
  title     = {{Issues in Bayesian Analysis of Neural Network Models}},
  author    = {Müller, Peter and Insua, David Ríos},
  journal   = {Neural Computation},
  year      = {1998},
  pages     = {749-770},
  doi       = {10.1162/089976698300017737},
  volume    = {10},
  url       = {https://mlanthology.org/neco/1998/muller1998neco-issues/}
}