Neural network modelling with input uncertainty:theory and application

Cornford, Dan; Wright, W.A.; Ramage, Guillaume and Nabney, Ian T. (2000). Neural network modelling with input uncertainty:theory and application. Journal of VLSI Signal Processing Systems for Signal Image and Video Technology, 26 (1-2), pp. 169-188.

Abstract

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network’s weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.

Publication DOI: https://doi.org/10.1023/A:1008111920791
Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Computer science research group
Engineering & Applied Sciences > Non-linearity and complexity research group
Related URLs:
Additional Information: The original publication is available at www.springerlink.com
Uncontrolled Keywords: Bayesian inference methods,neural networks,noise,errors in variable,Bayesian neural network framework,input noise,noise process exists,noiseless input,Markov chain Monte Carlo,satellite radar backscatter system,sea surface wind vectors,Information Systems,Signal Processing,Electrical and Electronic Engineering
Published Date: 2000

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