Markov Determinantal Point Process for Dynamic Random Sets
ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two
Christian Gouriéroux, Yang Lu
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In this chapter the emphasis of the discussion shifts from Laplace integrals \(\hat f(\lambda)\) and \(\hat dF(\lambda)\) to the Laplace transform \(\mathcal L : f \mapsto \hat f\) and to the Laplace-Stieltjes transform \(\mathcal Ls : F \mapsto \widehat {dF} \)
Wolfgang Arendt+3 more
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The Laplace Transformation [PDF]
The purpose of an analytic transformation is to change a more complicated problem into a simpler one. The Laplace transformation, which is applied chiefly with respect to the time variable, maps an IVP onto an algebraic equation or system. Once the latter is solved, its solution is fed into the inverse transformation to yield the solution of the ...
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The main applications of the Laplace transform are directed toward problems in which the time t is the independent variable. We shall therefore use this variable in this chapter. Let f(t) be a complex-valued function of the real variable t such that f(t)e -ct is abolutely integrable over 0 < t < ∞, where c is a real number.
Grant B. Gustafson, Calvin H. Wilcox
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