Computational philosophy of science
Computational philosophy of science
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
The nature of statistical learning theory
The nature of statistical learning theory
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
Using hidden nodes in Bayesian networks
Artificial Intelligence
Artificial intelligence and scientific method
Artificial intelligence and scientific method
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Foundations of Computational Linguistics: Man-Machine Communication in Natural Language
Foundations of Computational Linguistics: Man-Machine Communication in Natural Language
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Representation dependence in probabilistic inference
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Bayesian probability is normally defined over a fixed language or eventspace. But in practice language is susceptible to change, and thequestion naturally arises as to how Bayesian degrees of belief shouldchange as language changes. I argue here that this question poses aserious challenge to Bayesianism. The Bayesian may be able to meet thischallenge however, and I outline a practical method for changing degreesof belief over changes in finite propositional languages.