# Bayesian Learning for Neural Networks

@inproceedings{Neal1995BayesianLF, title={Bayesian Learning for Neural Networks}, author={Radford M. Neal}, year={1995} }

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigationâ€¦Â

## 3,440 Citations

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

- Computer Science
- 1995

Practical techniques based on Gaussian approximations for implementation of these powerful methods for controlling, comparing and using adaptive networks are described.

### Bayesian Neural Networks and GLM

- Computer ScienceSpringer Actuarial
- 2019

This work presents a probabilistic treatment of the authors' a priori knowledge about parameters based on Markov Chain Monte Carlo methods based on simulations, which results in a powerful framework that can be used for estimating the density of predictors.

### Hamiltonian Monte Carlo based on evidence framework for Bayesian learning to neural network

- Computer Science, MathematicsSoft Comput.
- 2019

This paper trains the network weights by means of Hamiltonian Monte Carlo (HMC) and proposes to sample from posterior distribution using HMC in order to approximate the derivative of evidence which allow to re-estimate hyperparameters.

### Bayesian Regularization of Neural Networks

- Computer ScienceArtificial Neural Networks
- 2009

This chapter outlines the equations that define the BRANN method plus a flowchart for producing a BRANN-QSAR model, and some results of the use of BRANNs on a number of data sets are illustrated and compared with other linear and nonlinear models.

### Model selection and model averaging for neural networks

- Computer Science
- 1998

This thesis develops a methodology for doing nonparametric regression within the Bayesian framework, and demonstrates how to use a noninformative prior for a neural network, which is useful because of the difficulty in interpreting the parameters.

### Classification using Bayesian neural nets

- Computer ScienceProceedings of International Conference on Neural Networks (ICNN'96)
- 1996

This paper demonstrates the effects of this approach by an implementation of the full Bayesian framework applied to two real world classification problems and discusses the idea of calibration to measure the predictive performance.

### Bayesian neural networks for classification: how useful is the evidence framework?

- Computer ScienceNeural Networks
- 1999

### A position paper on statistical inference techniques which integrate neural network and Bayesian network models

- Computer ScienceProceedings of International Conference on Neural Networks (ICNN'97)
- 1997

The Gibbs sampler is presented, both in its successful role as a convergence heuristic derived from statistical physics and under its probabilistic learning interpretation, and how the Bayesian network formalism informs the causal reasoning interpretation of some neural networks.

### Bayesian techniques for neural networks â€” Review and case studies

- Computer Science, Mathematics2000 10th European Signal Processing Conference
- 2000

This contribution gives a short review on Bayesian techniques for neural networks and presents comparison results from several case studies that include regression, classification, and inverse problems.

### Evolution programs for Bayesian training of neural networks

- Computer ScienceDefense, Security, and Sensing
- 1998

It is shown that Evolution Programs can be used to search the weight space for Bayesian training of a Neural Network using ANNs as classifiers, and the generalization to regression problems is straightforward.

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