The formal description of non-precise data before their statistical analysis is, except for error models and interval arithmetic, a relatively young topic. Fuzziness is described in the theory of fuzzy sets but only a few papers on statistical inference for non-precise data exist. In many cases, for example when very small concentrations are being measured, it is necessary to describe the imprecision of data. Otherwise, the results of statistical analysis can be unrealistic and misleading. Fortunately, there is a straightforward technique for dealing with non-precise data. The technique - the...
The formal description of non-precise data before their statistical analysis is, except for error models and interval arithmetic, a relatively young t...
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data.
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzzine...
Datenqualitat, Genauigkeit bzw. Ungenauigkeit von Daten und anderen Informationen sind grundlegende Aspekte von Messungen und Beobachtungen, die quantitativ beschrieben werden mussen, um unrealistische Resultate von Analysen zu vermeiden. In vielen praktischen Anwendungen erscheint die Angabe reeller Zahlen als vorliegende Datenelemente fragwurdig. Die Verwendung von unscharfen Zahlen ermoglicht es, die Unscharfe in die Modellbildung miteinzubeziehen und erlaubt somit eine realistischere Beschreibung von Daten.
Das Buch ist fur Leser geschrieben, die mit elementaren stochastischen...
Datenqualitat, Genauigkeit bzw. Ungenauigkeit von Daten und anderen Informationen sind grundlegende Aspekte von Messungen und Beobachtungen, die qu...
In recent years it has become apparent that an important part of the theory of Artificial Intelligence is concerned with reasoning on the basis of uncertain, incomplete or inconsistent information. Classical logic and probability theory are only partially adequate for this, and a variety of other formalisms have been developed, some of the most important being fuzzy methods, possibility theory, belief function theory, non monotonic logics and modal logics. The aim of this workshop was to contribute to the elucidation of similarities and differences between the formalisms mentioned above.
In recent years it has become apparent that an important part of the theory of Artificial Intelligence is concerned with reasoning on the basis of unc...
