We re®ne the ®rst theorem of (R. bounding the error of the ADA DABOOST OOST boosting algorithm, to integrate Bayes risk. This suggests the signi®cant time savings could be obtained on some domains without damaging the solution. An applicative example is given in the ®eld of feature selection.

Uncertainty is classically represented by probability functions, and diagnostic in an environment poised by uncertainty is usually handled through the application of the Bayesian theorem that permits the computation of the posterior probability over the diagnostic categories given the observed data from the prior probability over the same categories. We show here that the whole problem admits a...

Abstract We consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values parameters, once given as input to generative model, rarely lead simulations resembling observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all representation parameter’s posterior distribution. This leads a very large number required ...

The classical de Finetti theorem provides an operational definition of the concept of an unknown probability in Bayesian probability theory, where probabilities are taken to be degrees of belief instead of objective states of nature. In this paper, we motivate and review two results that generalize de Finetti’s theorem to the quantum mechanical setting: Namely a de Finetti theorem for quantum s...

The classical Bayesian posterior arises naturally as the unique solution of several different optimization problems, without the necessity of interpreting data as conditional probabilities and then using Bayes' Theorem. For example, the classical Bayesian posterior is the unique posterior that minimizes the loss of Shannon information in combining the prior and the likelihood distributions. The...