Dempster’s rule for combining two belief functions assumes the independence of the sources of information. If this assumption is questionable, I suggest to use the least specific combination minimizing the conflict among the ones allowed by a simple generalization of Dempster’s rule. This increases the monotonicity of the reasoning and helps us to manage situations of dependence. Some propertie...

This paper presents generalizations of Bayes likelihood-ratio updating rule which facilitate an asynchronous propagation of the impacts of new beliefs and/or new evidence in hierarchically organized inference structures with multi-hypotheses variables. The computational scheme proposed specifies a set of belief parameters, communication messages and updating rules which guarantee that the diffu...

Bayesian wavelet shrinkage methods are defined through a prior distribution on the space of wavelet coefficients after a Discrete Wavelet Transformation has been applied to the data. Posterior summaries of the wavelet coefficients establish a Bayes shrinkage rule. After the Bayes shrinkage is performed, an Inverse Discrete Wavelet Transformation can be used to recover the signal that generated ...

In data mining classification is to accurately predict the target class for each case in the data. Decision tree algorithm is one of the commonly used classification algorithm to make induction learning based on examples. In this paper we present the comparison of different classification techniques using WEKA. The aim of this paper is to investigate the performance of different classification ...

Jeffrey's rule of conditioning has been proposed in order to revise a probability measure by another probability function. We generalize it within the framework of the models based on belief functions. We show that several forms of Jeffrey's conditionings can be defined that correspond to the geometrical rule of conditioning and to Dempster's rule of conditioning, respectively. 1. Jeffrey's rul...

Wheeler WC and Pickett KM (2008. Topology-Bayes versus clade-Bayes in phylogenetic analysis. Mol Biol Evol. 25:447-453.) discuss two ways of summarizing the posterior probability distribution of a Bayesian phylogenetic analysis, which they refer to as "topology-Bayes" and "clade-Bayes." They claim that the clade-Bayes approach leads to problems such as "exaggerated clade support, inconsistently...

The most widely used updating rule for non-additive probabilities is the Dempster-Schafer rule. Schmeidler and Gilboa have developed a model of decision making under uncertainty based on non-additive probabilities, and in their paper "Updating Ambiguous Beliefs" they justify the Dempster-Schafer rule based on a maximum likelihood procedure. This note shows in the context of Schmeidler-Gilboa pr...

The general problem of inductive inference is to update from a prior probability distribution to a posterior distribution when new information becomes available. Bayes' rule is the natural way to update when the new information is in the form of data while Jaynes’ method of maximum entropy, MaxEnt, is designed to handle information in the form of constraints. However, the range of applicability...

This paper presents a new technique for the perception of activities using statistical description of spatio-temporal properties. With this approach, the probability of an activityin a spatio-temporal image sequence is computed by applying Bayes rule to the joint statistics of the responses of motion energy receptive fields. A set of motion energy receptive fields are designed in order to sampl...