Graphical models give a clear and concise way of describing dependencies among many variables. Only relationships among variables which all share a common hyperedge must be modeled, considerably simplifying both the modeling and the computational task. Graphical models have been studied by Pearl [1986a,1986b], Moussouris[1974], and Lauritzen and Spiegelhalter[1988] in the Bayesian case, and Kon...

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, the related operator commutes with Dempster’s sum of plausibility functions, while the relative belief perfectly rep...

Owning to the fact that ammunition can cause varying degrees of damage its target, this article presents a effectiveness calculation method hitting targets with based on Bayesian multinomial distribution solve problems complex processes, few trial times and difficult calculations probability in target-hitting tests high-tech ammunition, according index about occurrence different damage. Based c...

The notions of disintegration and Bayesian inversion are fundamental in conditional probability theory. They produce channels, as conditional probabilities, from a joint state, or from an already given channel (in opposite direction). These notions exist in the literature, in concrete situations, but are presented here in abstract graphical formulations. The resulting abstract descriptions are ...

We are interested in understanding the relationship between Bayesian inference and evidence theory, in particular imprecise and paradoxical reasoning. The concept of a set of probability distributions is central both in robust Bayesian analysis and in some versions of Dempster-Shafer theory. Most of the literature regards these two theories as incomparable. We interpret imprecise probabilities ...

This paper presents an applied practical comparison of Bayesian and Dempster-Shafer techniques useful for managing uncertainty in sensing. Three formulations of the same example are presented: a Bayesian, a naive Dempster-Shafer, and a Dempster-Shafer approach using a reened frame of discernment. Both the Bayesian and Dempster-Shafer (with a reened frame of discernment) yield similar results; h...

We review an established Bayesian sampling method called sampling/importance resampling and highlight situations in nuclear theory when it can be particularly useful. To this end we both analyse a toy problem demonstrate realistic applications of importance to infer the posterior distribution for parameters $\Delta$NNLO interaction model based on chiral effective field estimate probability targ...

Several, apparently incomparable, approaches exist for uncertainty management. Uncertainty management is a broad area applied in many different fields, where information about some underlying, not directly observable, truth–the state of the world–is sought from a set of observations that are more or less reliable. These observations can be, for example, measurements with random and/or systemati...