Cited by 915Gaussian Processes and Bayesian InferenceNeural Networks and ApplicationsBayesian Modeling and Causal Inference

Probable networks and plausible predictions — a review of practical Bayesian methods for supervised neural networks

Mackay, David J C·1995·Network Computation in Neural Systems

Abstract

Bayesian probability theory provides a unifying framework for data modelling. In this framework the overall aims are to find models that are well-matched to the data, and to use these models to make optimal predictions. Neural network learning is interpreted as an inference of the most probable parameters for the model, given the training data. The search in model space (i.e., the space of architectures, noise models, preprocessings, regularizers and weight decay constants) can then also be treated as an inference problem, in which we infer the relative probability of alternative models, given the data. This review describes practical techniques based on Gaussian approximations for implementation of these powerful methods for controlling, comparing and using adaptive networks.

Integrity signals

Registry

Crossref · 10.1088/0954-898x_6_3_011

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • This form of model selection implements automatic relevance determination by optimising the model evidence with respect to model hyperparameters.

    “This reflects the fact that this form of model selection implements automatic relevance determination (MacKay 1995), by virtue of optimising the model evidence with respect to model hyperparameters; in this instance, the shrinkage priors prescribed by an adjacency matrix.”

    From Network discovery with DCM (2011) · cites this paper as MacKay 1995

Cites

Flagged first, then by citations

2 of the 52 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Mackay, D. J. C.
Affiliations
University of Cambridge (GB)
Corresponding author
Mackay, David J C
Venue
Network Computation in Neural Systems
Published
1995-01
Volume
6
Issue
3
Pages
469-505
ISSN
  • 0954-898X (ISSN-L)
  • 1361-6536
OpenAlex
W2139701068
Cited by
915
References
52
Citations by year
2026: 14 · 2025: 33 · 2024: 28 · 2023: 40 · 2022: 50 · 2021: 39 · 2020: 58 · 2019: 40 · 2018: 36 · 2017: 27 · 2016: 17 · 2015: 36 · 2014: 24 · 2013: 29 · 2012: 39
FWCI
13.31
Type
journal article
Language
en
Open access
closed
Locations
  • Network Computation in Neural Systems (publishedVersion)
  • Cambridge University Engineering Department Publications Database (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords
Bayesian neural networks, supervised learning, Gaussian approximation, model selection, model space search, parameter inference, adaptive networks, noise models, regularization, weight decay

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review