By Bernadette Bouchon-Meunier, Giulianella Coletti, Ronald R. Yager
The quantity "Modern info Processing: From idea to Applications," edited through Bernadette Bouchon-Meunier, Giulianella Coletti and Ronald Yager, is a set of rigorously chosen papers drawn from this system of IPMU'04, which was once held in Perugia, Italy.
The e-book represents the cultural coverage of IPMU convention which isn't keen on slim diversity of methodologies, yet to the contrary welcomes all of the theories for the administration of uncertainty and aggregation of data in clever platforms, supplying a medium for the alternate of principles among theoreticians and practitioners in those and comparable parts.
The publication consists via 7 sections:
UNCERTAINTY
PREFERENCES
CLASSIFICATION and information MINING
AGGREGATION AND MULTI-CRITERIA choice MAKING
KNOWLEDGE REPRESENTATION
•The ebook contributes to enhancement of our skill to deal successfully with uncertainty in all of its manifestations.
•The publication will help to construct brigs between theories and strategies equipment for the administration of uncertainty.
•The e-book addresses concerns that have a place of centrality in our information-centric global.
•The booklet provides attention-grabbing effects dedicated to representing wisdom: the aim is to seize the subtlety of human wisdom (richness) and to permit laptop manipulation (formalization).
•The ebook contributes to the aim: a good use of the data for an excellent selection strategy.
APPLIED DOMAINS
· The booklet contributes to enhancement of our skill to deal successfully with uncertainty in all of its manifestations.
· The e-book may also help to construct brigs between theories and techniques tools for the administration of uncertainty.
· The e-book addresses concerns that have a place of centrality in our information-centric world.
· The publication provides fascinating effects dedicated to representing wisdom: the target is to catch the subtlety of human wisdom (richness) and to permit desktop manipulation (formalization).
· The publication contributes to the aim: a good use of the data for an excellent selection approach.
Read Online or Download Modern Information Processing. From Theory to Applications PDF
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Extra resources for Modern Information Processing. From Theory to Applications
Sample text
Prade, H. The mean value of a fuzzy number. Fuzzy Sets & Systems, 24, 279-300,1987. 11. , Akcakaya, R. Whereof one cannot speak: when input distributions are unknown. To appear in Risk Analysis. 12. Person, S. What Monte Carlo methods cannot do. Human and Ecology Risk Assessment, 2, 990-1007,1996. 13. R. Different methods are needed to propagate ignorance and variability. Reliability Engineering and Systems Safety, 54, 133-144,1996. 14. R. Hybrid Arithmetic. Proceedings of ISUMA-NAFIPS'95, IEEE Computer Society Press, Los Alamitos, California, 619-623,1995.
Fuzzy Sets and Systems, 1, 1978, 283-297. 12. , Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, 1978, 3-28. Modem Information Processing: From Theory to Applications B. Bouchon-Meunier, G. R. V. All rights reserved 37 Joint Treatment of Imprecision and Randomness in Uncertainty Propagation C. Baudrit^ and D. Dubois^ and D. Guyonnet^ and H. Fargier^ ^Institut de Recherche en Informatique de Toulouse, Universite Paul Sabatier Toulouse, France ^Service Environnement et Precedes, BRGM, Orleans, France Abstract This paper presents and studies in detail a hybrid method of uncertainty propagation for the case where knowledge regarding some parameters of a physical model is represented by probability measures, while others are represented by possibility measures or belief functions.
Belief-plausibility transformations Another brand new belief-plausibility transformation BelPLT BelPlJ>iA) = "^(^) + g ( ^ ) is defined as ^^^^^. BelPLT is not ulb-consistent , it is only p-consistent in general. Unfortunately it is neither 0-consistent nor ©/-consistent nor ©-consistent. The analogy of BelPLT is defined as PI - Bel P(A) - ^^(-^) - "^(-^) Pl-BeLT is not ulb-consistent, it is not defined for Bayesian BFs, and it is neither 0-consistent nor ©-consistent. In this subsection it is necessary to mention also a transformation presented in [1] which is based on —^ ^ • This fraction does not make a probability in general (on the other hand in the 2D case it is equal to 2DBetT).