On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
The direct use of likelihood for significance testing
Statistics and Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation
The Journal of Machine Learning Research
| Hi-index | 0.00 |
Focussed Bayesian fusion reduces high computational costs caused by Bayesian fusion by restricting the range of the Properties of Interest which specify the structure of the desired information on its most task relevant part. Within this publication, it is concisely explained how Bayesian theory and the theory of statistical evidence can be combined to derive meaningful focussed Bayesian models and to rate the validity of a focussed Bayesian analysis quantitatively. Earlier results with regard to this topic will be further developed and exemplified.