The authors use Bayesian probability theory to extend the ``QBism'' interpretation of quantum mechanics by showing how one may derive Born's rule from a set assumptions where state and measurement are described relative ``reference measurement.'' result gives insight into rule, even if does not support subjective underlying QBism.

This paper concerns the extent to which uncertain propositional reasoning can track probabilistic reasoning, and addresses kinematic problems that extend the familiar Lottery paradox. An acceptance rule assigns to each Bayesian credal state p a propositional belief revision method Bp, which specifies an initial belief state Bp(⊤), that is revised to the new propositional belief state B(E) upon ...

In this paper we prove that a recent Bayesian approximation of belief functions, the relative belief of singletons, meets a number of properties with respect to Dempster’s rule of combination which mirrors those satisfied by the relative plausibility of singletons. In particular, its operator commutes with Dempster’s sum of plausibility functions, while perfectly representing a plausibility fun...

the differential information literature in economics and game theory? Why has the those posteriors are derived by updating the common prior. Perhaps the most world can be generated by conditioning by Bayes rule. If there are many. Find a weak perfect bayesian equilibrium (strategies and beliefs) where the be arbitrary (and thus don't have to be according to Bayesian updating) but I am under Bro...

The paper describes an inductive learning environment called DELVAUX for classiication tasks that learns PROSPECTOR-style, Bayesian rules from sets of examples. A genetic algorithm approach is used for learning Bayesian rule-sets, in which a population consists of sets of rule-sets that generate oosprings through the exchange of rules, permitting tter rule-sets to produce oosprings with a highe...

The naive Bayesian classiier provides a simple and eeective approach to classiier learning, but its attribute independence assumption is often violated in the real world. A number of approaches have sought to alleviate this problem. A Bayesian tree learning algorithm builds a decision tree, and generates a local naive Bayesian classiier at each leaf. The tests leading to a leaf can alleviate at...

A medical diagnosis system (DRCAD), which consists of two sub-modules Bayesian and rule-based inference models, is presented in this study. Three types of tests are conducted to assess the performances of the models producing synthetic data based on the ALARM network. The results indicate that the linear combination of the aforementioned models leads to a 5% and a 30% improvement in medical dia...

In this paper we examine the use of Bayesian networks (BNs) for improving weather prediction, applying them to the problem of predicting sea breezes. We compare a pre-existing Bureau of Meteorology rule-based system with an elicited BN and others learned by two data mining programs, TETRAD II Spirtes et al., 1993] and Causal MML Wallace and Korb, 1999]. These Bayesian nets are shown to signiica...