# Measurement Uncertainty: An Approach Via the Mathematical by Simona Salicone By Simona Salicone

The expression of uncertainty in dimension is a demanding point for researchers and engineers operating in instrumentation and size since it consists of actual, mathematical and philosophical matters. This challenge is intensified by way of the restrictions of the probabilistic procedure utilized by the present typical (GUM). this article is the 1st to make complete use of the mathematical concept of facts to specific the uncertainty in measurements. It provides an outline of the present average, then pinpoints and constructively resolves its obstacles via its new angle. The textual content provides numerous instruments for comparing uncertainty, starting with the probabilistic technique and concluding with the expression of uncertainty utilizing random-fuzzy variables. The exposition is pushed through a number of examples. The e-book is designed for fast use and alertness in learn and laboratory paintings. must haves for college kids contain classes in records and size technological know-how. except a school room environment, this e-book can be utilized by way of practitioners in a number of fields (including utilized arithmetic, utilized chance, electric and desktop engineering, and experimental physics), and via such associations because the IEEE, ISA, and nationwide Institute of criteria and know-how.

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Additional info for Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence

Sample text

Let K be an arbitrary index set. 27) k∈K k∈K for any family {Ak | k ∈ K} in P (X). Having stated that the necessity and possibility functions can be defined in the more general case of sets with an infinite number of elements, let us now go back to the simpler case of finite sets, where all mathematical definitions are much simpler. This can be done without lacking generality, because all mathematical definitions can be readily extended to the infinite sets thanks to Eqs. 27). As necessity functions are special belief functions and possibility functions are special plausibility functions, they must satisfy Eqs.

In fact, for each subset A of X, the number Bel(A) can be interpreted as the degree of belief of a person that the truth lies in A and the preceding rules can be considered as rules governing these degrees of belief. In order to understand the meaning of degree of belief, let us remember Shafer’s words as reported in his book [S76]: ‘whenever I write of the ‘degree of support’ that given evidence provides for a proposition or of the ‘degree of belief’ that an individual accords to the proposition, I picture in my mind an act of judgment.

7) for any α between 0 and 1. Let us remember that, when the division is considered, it is required that 0 ∈ / Bα for every α between 0 and 1. 24 2 Fuzzy Variables and Measurement Uncertainty Fig. 4. Arithmetic operations on fuzzy variables: (a) addition, (b) subtraction, (c) multiplication, and (d) division. For the sake of clearness, let us consider the three fuzzy variables A, B, and C, where C = A ∗ B, and let us denote the generic α-cuts of A, B, and α α α α α C as Aα = [aα 1 , a2 ] , B = [b1 , b2 ], and C = [c1 , c2 ], respectively.