no code implementations • 9 Nov 2018 • Mieczysław Kłopotek
Weaknesses of Shafer's proposal \cite{Shafer:90b} of probabilistic interpretation of MTE belief functions is demonstrated.
no code implementations • 9 Nov 2018 • Mieczysław Kłopotek
In this paper we develop a frequentist model of the MTE bringing to fall the above argument against MTE.
no code implementations • 29 May 2017 • Mieczysław Kłopotek
Fortunately, if we deal with causally sufficient sets of variables (that is whenever significant influence variables are not omitted from observation), then there exists the possibility to identify the family of belief networks a causal network belongs to [16].
no code implementations • 2 May 2017 • Mieczysław Kłopotek
A method to recover structural parameters of looped jointed objects from multiframes is being developed.
no code implementations • 13 Apr 2017 • Mieczysław Kłopotek
We develop our interpretation of the joint belief distribution and of evidential updating that matches the following basic requirements: * there must exist an efficient method for reasoning within this framework * there must exist a clear correspondence between the contents of the knowledge base and the real world * there must be a clear correspondence between the reasoning method and some real world process * there must exist a clear correspondence between the results of the reasoning process and the results of the real world process corresponding to the reasoning process.
no code implementations • 6 Apr 2017 • Mieczysław Kłopotek
This paper suggests a new interpretation of the Dempster-Shafer theory in terms of probabilistic interpretation of plausibility.
no code implementations • 15 Feb 2017 • Robert Kłopotek, Mieczysław Kłopotek
This paper investigates the validity of Kleinberg's axioms for clustering functions with respect to the quite popular clustering algorithm called $k$-means.
no code implementations • 13 Feb 2017 • Mieczysław Kłopotek
This paper investigates the application of consensus clustering and meta-clustering to the set of all possible partitions of a data set.