The Bayesian model comparison framework is reviewed, and the Bayesian Occam's razor is explained. This framework can be applied to feedforward networks, making possible (1) objective comparisons between solutions using alternative network architectures; (2) objective choice of magnitude and type of weight decay terms; (3) quantified estimates of the error bars on network parameters and on network output. The framework also generates a measure of the effective number of parameters determined by the data. The relationshi
<p>The bar plot in the upper panel (a) summarizes the comparison of the five model families in terms...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the...
mackayGras.phy.cam.ac.uk The Bayesian model comparison framework is reviewed, and the Bayesian Occam...
The Bayesian model comparison framework is reviewed, and the Bayesian Occam's razor is explained. Th...
A quantitative and practical Bayesian framework is described for learn-ing of mappings in feedforwar...
A quantitative and practical Bayesian framework is described for learning of mappings in feedforward...
thodberg~nn.meatre.dk MacKay's Bayesian framework for backpropagation is conceptually appealing...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
<p>The degree to which a network's state encodes the Bayesian posterior via a logistic model is show...
Bayesian networks (BNs) are increasingly used to model environmental systems, in order to: integrate...
A Bayesian network is a widely used probabilistic graphicalmodel with applications in knowledge disc...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
A Bayesian method for the comparison and selection of multi-output feedforward neural network topolo...
<p>The bar plot in the upper panel (a) summarizes the comparison of the five model families in terms...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the...
mackayGras.phy.cam.ac.uk The Bayesian model comparison framework is reviewed, and the Bayesian Occam...
The Bayesian model comparison framework is reviewed, and the Bayesian Occam's razor is explained. Th...
A quantitative and practical Bayesian framework is described for learn-ing of mappings in feedforwar...
A quantitative and practical Bayesian framework is described for learning of mappings in feedforward...
thodberg~nn.meatre.dk MacKay's Bayesian framework for backpropagation is conceptually appealing...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
<p>The degree to which a network's state encodes the Bayesian posterior via a logistic model is show...
Bayesian networks (BNs) are increasingly used to model environmental systems, in order to: integrate...
A Bayesian network is a widely used probabilistic graphicalmodel with applications in knowledge disc...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
A Bayesian method for the comparison and selection of multi-output feedforward neural network topolo...
<p>The bar plot in the upper panel (a) summarizes the comparison of the five model families in terms...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the...