Add to ORKGthis paper is illustrated in figure 6e. If we give a probabilistic interpretation to the model, then we can evaluate the `evidence' for alternative values of the control parameters. Over-complex models turn out to be less probable, and the quantit
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes....
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
This is a Whitepaper submitted to the 2017 DOE ASCR Applied Math Meeting. It contains a proposal to ...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
Approximate marginal Bayesian computation and inference are developed for neural network models. The...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
When reasoning in the presence of uncertainty there is a unique and self consistent set of rules for...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
The Bayesian evidence framework has become a standard of good practice for neural network estimation...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
When reasoning in the presence of uncertainty there is a unique and self-consistent set of rules for...
this article may suggest that the research reported herein may be more fundamental than it really is...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes....
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
This is a Whitepaper submitted to the 2017 DOE ASCR Applied Math Meeting. It contains a proposal to ...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
Approximate marginal Bayesian computation and inference are developed for neural network models. The...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
When reasoning in the presence of uncertainty there is a unique and self consistent set of rules for...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
The Bayesian evidence framework has become a standard of good practice for neural network estimation...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
When reasoning in the presence of uncertainty there is a unique and self-consistent set of rules for...
this article may suggest that the research reported herein may be more fundamental than it really is...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes....
In recent years, Neural Networks (NN) have become a popular data-analytic tool in Statistics, Compu...
This is a Whitepaper submitted to the 2017 DOE ASCR Applied Math Meeting. It contains a proposal to ...