## Quote of the day: On Neural Networks and Bayesian Statistics

### May 7, 2008 Posted by Emre S. Tasci

"The two ideas of neural network modelling and Bayesian statistics might seem uneasy bed-fellows. Neural networks are non-linear parallel computational devices inspired by the structure of the brain. ‘Backpropagation networks’ are able to learn, by example, to solve prediction and classification problems. Such a neural network is typically viewed as a black box which finds by hook or by crook an incomprehensible solution to a poorly understood problem. In contrast, Bayesian methods are characterized by an insistence on coherent inference based on clearly defined axioms; in Bayesian circles, and ‘ad hockery’ is a capital offense. Thus Bayesian statistics and neural networks might seem to occupy opposite extremes of the data modelling spectrum."

**Probable Networks and Plausible Predictions – A Review of Practical Bayesian Methods for Supervised Neural Networks ***

**David J.C. MacKay ***

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