Bayesian Interpolation

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Bayesian Interpolation (1991)

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by David J.C. MacKay

Venue:	NEURAL COMPUTATION
Citations:	726 - 17 self

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@ARTICLE{MacKay91bayesianinterpolation,
    author = {David J.C. MacKay},
    title = {Bayesian Interpolation},
    journal = {NEURAL COMPUTATION},
    year = {1991},
    volume = {4},
    pages = {415--447}
}

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Abstract

Although Bayesian analysis has been in use since Laplace, the Bayesian method of model--comparison has only recently been developed in depth. In this paper, the Bayesian approach to regularisation and model--comparison is demonstrated by studying the inference problem of interpolating noisy data. The concepts and methods described are quite general and can be applied to many other problems. Regularising constants are set by examining their posterior probability distribution. Alternative regularisers (priors) and alternative basis sets are objectively compared by evaluating the evidence for them. `Occam's razor' is automatically embodied by this framework. The way in which Bayes infers the values of regularising constants and noise levels has an elegant interpretation in terms of the effective number of parameters determined by the data set. This framework is due to Gull and Skilling.

Keyphrases

bayesian interpolation model comparison central task bayesian method effective number alternative basis set bayesian analysis noise level data set data modelling bayesian approach posterior probability distribution alternative regularisers many problem noisy data elegant interpretation inference problem

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