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

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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

DOI 10.1088/0954-898x/6/3/011

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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.

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Achterhold, J., Koehler, J. M., Schmeink, A., & Genewein, T. (2018) · International Conference on Learning Representations · Cited by 60

Authors: Mackay, D. J. C.
Affiliations: University of Cambridge (GB)
Corresponding author: Mackay, David J C
ORCID: Mackay, David J C
Venue: Network Computation in Neural Systems
Publisher: Informa UK Limited
Published: 1995-01
Volume: 6
Issue: 3
Pages: 469-505
ISSN: 0954-898X (ISSN-L)
1361-6536
DOI: 10.1088/0954-898x/6/3/011
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

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

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