This paper explores the impact of the assumption of unambiguous types on the Dempster-Shafer equilibrium introduced by Eichberger and Kelsey (2004). It is shown that if the types of the Sender are perceived unambiguous, then the Receiver’s conditional Choquet preference derived by the DempsterShafer updating rule is expected utility, regardless of whether the observed massage is ambiguous or no...

This paper characterizes several belief-revision rules in a uni…ed framework: Bayesian revision upon learning some event, Je¤rey revision upon learning new probabilities of some events, Adams revision upon learning some new conditional probabilities, and ‘dual-Je¤rey’revision upon learning a new conditional probability function. Despite their di¤erences, these revision rules can be characterize...

In the application of discriminant analysis, a situation sometimes arises where individual measurements are screened by a multidimensional screening scheme. For this situation, a discriminant analysis with screened populations is considered from a Bayesian viewpoint, and an optimal predictive rule for the analysis is proposed. In order to establish a flexible method to incorporate the prior inf...

In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL) paradigm where expert opinions can be encoded in the form of fuzzy rule bases and the hyper-parameters of the fuzzy sets can be learned from data using a Bayesian approach. The present paper extends this work for selecting the most appropriate rule base among a set of competing alternatives, which best explains the data, by c...

This paper introduces a new algorithm called “Adaptive Multimodal Biometric Fusion Algorithm”(AMBF), which is a combination of Bayesian decision fusion and particle swarm optimization. A Bayesian framework is implemented to fuse decisions received from multiple biometric sensors. The system’s accuracy improves for a subset of decision fusion rules. The optimal rule is a function of the error co...

In this report we analyze different model selection criteria that were used in statistics, reconstruction, recognition and other areas. We concentrate on criteria based on Bayesian theory, MDL theory, and predicting future residuals. Our analysis showed that these criteria often are not consistent when the number of points in the underlying data of the model is changed or if the scale of the da...

The mathematical principles of quantum theory provide a general foundation for assigning probabilities to events. This paper examines the application of these principles to the probabilistic inference problem in which hypotheses are evaluated on the basis of a sequence of evidence (observations). The probabilistic inference problem is usually addressed using Bayesian updating rules. Here we der...

One of the fundamental problems of epistemology is to say when the evidence in an agent’s possession justifies the beliefs she holds. In this paper and its prequel, we defend the Bayesian solution to this problem by appealing to the following fundamental norm: Accuracy An epistemic agent ought to minimize the inaccuracy of her partial beliefs. In the prequel, we made this norm mathematically pr...

There are different methods of statistical hypotheses testing. 1 – 4 Among them, a special place has Bayesian approach. A generalization of Bayesian rule of many hypotheses testing is given below. It consists in increasing of decision rule dimensionality with respect to the number of tested hypoteses, which allows to make decisions more differentiated than in the classical case and to state, in...

Adaptive control problems are notoriously difficult to solve even in the presence of plantspecific controllers. One way to by-pass the intractable computation of the optimal policy is to restate the adaptive control as the minimization of the relative entropy of a controller that ignores the true plant dynamics from an informed controller. The solution is given by the Bayesian control rule— a s...