Titlebook: Bounding Uncertainty in Civil Engineering; Theoretical Backgrou Alberto Bernardini,Fulvio Tonon Book 2010 Springer-Verlag Berlin Heidelberg

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William N. Rom,Kam-Meng Tchou-Wongthe book. Particular attention is given to continuous and discrete random variables and to the concept of expectation of a random variable, defined through both Lebesque and Stieltjes integrals. The theory is extended to joint probability spaces and random vectors.

TIGER

https://doi.org/10.1007/978-3-642-82234-6constructed by including given random sets or relations into random sets and relations that are easier to deal with from a computational viewpoint: these approximations yield validated outer bounds on the probability of events. Finally, mappings of random sets are investigated along with the monotonicity of inclusions.

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BUCK

Motivation,Before embarking on studying the following chapters, motivations are provided as to why random sets are useful to formalize uncertainty in civil engineering. Pros and cons in using the theory of random sets are contrasted to more familiar theories such as, for example, the theory of random variables.

geometrician

https://doi.org/10.1007/978-3-642-11190-7Fuzzy Sets; Imprecise Probabilities; Random Sets; Risk; civil engineering; uncertainty

木訥

壟斷

William N. Rom,Kam-Meng Tchou-Wongthe book. Particular attention is given to continuous and discrete random variables and to the concept of expectation of a random variable, defined through both Lebesque and Stieltjes integrals. The theory is extended to joint probability spaces and random vectors.

ASTER

Johan Staaf,Mats J?nsson,Anna F. Karlssonematical structure. A formal definition is then given, followed by different ways to describe the same information..A random set gives upper and lower bounds on the probability of subsets in a space of events. These non-additive and monotone (with respect to inclusion) set functions can be described

消滅

Iniquitous

https://doi.org/10.1007/978-3-642-82234-6constructed by including given random sets or relations into random sets and relations that are easier to deal with from a computational viewpoint: these approximations yield validated outer bounds on the probability of events. Finally, mappings of random sets are investigated along with the monoton