Titlebook: Information Processing and Management of Uncertainty in Knowledge-Based Systems; 18th International C Marie-Jeanne Lesot,Susana Vieira,Rona

Blanch

Coherent and Archimedean Choice in General Banach Spacesiated with Archimedean choice models, and also pay quite a lot of attention to representation of general (non-binary) choice models in terms of the simpler, binary ones. The representation theorems proved here provide an axiomatic characterisation of, amongst other choice methods, Levi’s E-admissibility and Walley–Sen maximality.

Licentious

Dynamic Portfolio Selection Under Ambiguity in the ,-Contaminated Binomial Modelion in terms of the terminal wealth, and provide a characterization of the optimal solution in the case stock price returns are uniformly distributed. In this case, we further investigate the effect of the contamination parameter . on the optimal portfolio.

乐意

Archimedean Choice Functions choice functions that uniquely characterises this rule, thereby providing an axiomatic foundation for imprecise decision making with sets of probabilities. A representation theorem for Archimedean choice functions in terms of coherent lower previsions lies at the basis of both results.

propose

1865-0929 ement of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually...The 173 papers were carefully reviewed and selected from 213 submissions. The papers

conscience

New Results in the Calculus of Fuzzy-Valued Functions Using Mid-Point Representationsfferentiability of first and second orders have nice and useful midpoint expressions. Using mid-point representation of fuzzy-valued functions, partial orders and properties of monotonicity and convexity are discussed and analysed in detail. Periodicity is easy to represent and identify. Graphical examples and pictures accompany the presentation.

背信

On the Elicitation of an Optimal Outer Approximation of a Coherent Lower Probabilityimation: minimising a number of distances with respect to the initial model, or maximising the specificity of the outer approximation. We apply these to 2- and completely monotone approximating lower probabilities, and also to possibility measures.

nauseate

Learning Sets of Bayesian Networksle size (ESS), the approach also considers the possibility of an undetermined ESS. Even if the final result is a set of Bayesian networks, the paper also studies the problem of selecting a single network with some alternative procedures. Finally, some preliminary experiments are carried out with three small networks.

infelicitous

deviate

carotenoids