Hannu Nurmi (1982)
Stochastica
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This paper deals with two ways in which uncertainty notions enter social science models: 1) They can be used in an effort to make intelligible some phenomena that would otherwise be difficult to comprehend, or 2) They can be use to generalize or modify the domain of validity of some theoretical results.
Heinz J. Skala (1982)
Stochastica
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After a short discussion of the first application of measure theoretic tools to economics we show that it is consistent relative to the usual axioms of set theory that there exists no nonatomic probability space of power less than the continuum. This together with other results shows that Aumann's continuum-of-agents methodology provides a sound framework at least for the cooperative theory. There are, however, other problems in economics where, without further assumptions,...
David Miller (1982)
Stochastica
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J. Matkowski, M. Sablik (1986)
Stochastica
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Equation [1] f(x+y) + f (f(x)+f(y)) = f (f(x+f(y)) + f(f(x)+y)) has been proposed by C. Alsina in the class of continuous and decreasing involutions of (0,+∞). General solution of [1] is not known yet. Nevertheless we give solutions of the following equations which may be derived from [1]: [2] f(x+1) + f (f(x)+1) = 1, [3] f(2x) + f(2f(x)) = f(2f(x + f(x))). Equation [3] leads to a Cauchy functional equation: ...
Barbara Baccheli (1986)
Stochastica
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Strengthened forms of Ling's representation theorem concerning a class of continuous associative functions are given: Firstly the monotonicity condition is removed. Then the associativity condition is replaced by the power associativity.
Mirko Polonijo (1985)
Stochastica
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The notion of a TST-space is introduced and its connection with a parallelogram space is given. The existence of a TST-space is equivalent to the existence of a parallelogram space, which is a new characterization of a parallelogram space. The structure of a TST-space is described in terms of an abelian group.
José Juan Quesada (1986)
Stochastica
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In this paper we will prove a characterization for the independence of random vectors with positive (negative) orthant dependence according to a direction. The result can be seen as a generalization of a result by Lehmann [4].
Gian Luigi Forti (1985)
Stochastica
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Tao Liao, Yongjie Zhang, Peter M. Kekenes-Huskey, Yuhui Cheng, Anushka Michailova, Andrew D. McCulloch, Michael Holst, J. Andrew McCammon (2013)
Molecular Based Mathematical Biology
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Multi-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaussian kernel functions is employed to generate implicit models for biomolecules....