Bayesian approach for neural networks—review and case studies

Lampinen, Jouko; Vehtari, Aki

doi:10.1016/s0893-6080(00)00098-8

Cited by 392Gaussian Processes and Bayesian InferenceNeural Networks and ApplicationsStatistical Methods and Bayesian Inference

Bayesian approach for neural networks—review and case studies

Lampinen, Jouko, Vehtari, Aki·2001·Neural Networks

DOI 10.1016/s0893-6080(00)00098-8

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Abstract

We give a short review on the Bayesian approach for neural network learning and demonstrate the advantages of the approach in three real applications. We discuss the Bayesian approach with emphasis on the role of prior knowledge in Bayesian models and in classical error minimization approaches. The generalization capability of a statistical model, classical or Bayesian, is ultimately based on the prior assumptions. The Bayesian approach permits propagation of uncertainty in quantities which are unknown to other assumptions in the model, which may be more generally valid or easier to guess in the problem. The case problem studied in this paper include a regression, a classification, and an inverse problem. In the most thoroughly analyzed regression problem, the best models were those with less restrictive priors. This emphasizes the major advantage of the Bayesian approach, that we are not forced to guess attributes that are unknown, such as the number of degrees of freedom in the model, non-linearity of the model with respect to each input variable, or the exact form for the distribution of the model residuals.

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Bayesian Neural Networks are stochastic artificial neural networks trained with Bayesian inference.
“Bayesian Neural Networks (BNNs) [68,71,72] are stochastic ANNs trained using Bayesian inference.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [71]
BNNs require complex modifications to training process.
“Requires complex modifications to training process”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [71]
BNNs are intrinsically interpretable.
“Intrinsically interpretable”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [71]

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Authors: Lampinen, J., & Vehtari, A.
Corresponding author: Lampinen, Jouko
ORCID: Vehtari, Aki
Venue: Neural Networks
Publisher: Elsevier BV
Published: 2001-04
Volume: 14
Issue: 3
Pages: 257-274
ISSN: 0893-6080 (ISSN-L)
DOI: 10.1016/s0893-6080(00)00098-8
OpenAlex: W2040355924
Cited by: 392
References: 62
Citations by year: 2026: 27 · 2025: 38 · 2024: 35 · 2023: 32 · 2022: 21 · 2021: 15 · 2020: 16 · 2019: 12 · 2018: 14 · 2017: 8 · 2016: 15 · 2015: 9 · 2014: 12 · 2013: 7 · 2012: 3
FWCI: 10.39
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Neural Networks (publishedVersion)
PubMed (publishedVersion)
Funders: Tekes
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: Bayesian neural networks, prior knowledge, error propagation, generalization, regression analysis, classification, inverse problems, model complexity, residual distribution, error reduction
MeSH terms: Algorithms, Bayes Theorem, Neural Networks, Computer

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