Add to ORKGThe goal of this paper is to compare the similarities and differences between Bayesian and belief function reasoning. Our main conclusion is that although there are obvious differences in semantics, representations, the rules for combining and marginalizing representations, there are many similarities. We claim that the two calculi have roughly the same expressive power. Each calculus has its own semantics that allow us to construct models suited for these semantics. Once we have a model in either calculus, one can transform it to the other by means of a suitable transformation
An often mentioned obstacle for the use of Dempster-Shafer theory for the handling of uncertainty i...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...
The goal of this paper is to compare the similarities and differences between Bayesian and belief fu...
AbstractThe theory of belief functions is a generalization of the Bayesian theory of subjective prob...
In this paper, we propose the plausibility transformation method for translating Dempster-Shafer (D-...
Bayesian Belief Networks are graph-based representations of probability distributions. In the last d...
This 12-pp paper is extracted from a longer unpublished working paper: "On Transforming Belief Funct...
The main purpose of this article is to introduce the evidential reasoning approach, a research meth...
Belief updating schemes in artificial intelligence may be viewed as three dimensional languages, con...
In developing methods for dealing with uncertainty in reasoning systems, it is important to consider...
Evidential Reasoning (ER), based on the Dempster-Schafer theory of evidence, and Bayesian Networks (...
By identifying and pursuing analogies between causal and logical in uence I show how the Bayesian ne...
Rule-based expert systems must deal with uncertain data, subjective expert opinions, and inaccurate ...
The Dempster-Shafer theory is being applied for handling uncertainty in various domains. Many method...
An often mentioned obstacle for the use of Dempster-Shafer theory for the handling of uncertainty i...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...
The goal of this paper is to compare the similarities and differences between Bayesian and belief fu...
AbstractThe theory of belief functions is a generalization of the Bayesian theory of subjective prob...
In this paper, we propose the plausibility transformation method for translating Dempster-Shafer (D-...
Bayesian Belief Networks are graph-based representations of probability distributions. In the last d...
This 12-pp paper is extracted from a longer unpublished working paper: "On Transforming Belief Funct...
The main purpose of this article is to introduce the evidential reasoning approach, a research meth...
Belief updating schemes in artificial intelligence may be viewed as three dimensional languages, con...
In developing methods for dealing with uncertainty in reasoning systems, it is important to consider...
Evidential Reasoning (ER), based on the Dempster-Schafer theory of evidence, and Bayesian Networks (...
By identifying and pursuing analogies between causal and logical in uence I show how the Bayesian ne...
Rule-based expert systems must deal with uncertain data, subjective expert opinions, and inaccurate ...
The Dempster-Shafer theory is being applied for handling uncertainty in various domains. Many method...
An often mentioned obstacle for the use of Dempster-Shafer theory for the handling of uncertainty i...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...