52,912 research outputs found

    Noncommutative Burkholder/Rosenthal inequalities II: applications

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    We show norm estimates for the sum of independent random variables in noncommutative LpL_p-spaces for 1<p<1<p<\infty following our previous work. These estimates generalize the classical Rosenthal inequality in the commutative case. Among applications, we derive an equivalence for the pp-norm of the singular values of a random matrix with independent entries, and characterize those symmetric subspaces and unitary ideals which can be realized as subspaces of a noncommutative LpL_p for 2<p<2<p<\infty.Comment: To appear in Isreal J; Mat

    Forward Analysis and Model Checking for Trace Bounded WSTS

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    We investigate a subclass of well-structured transition systems (WSTS), the bounded---in the sense of Ginsburg and Spanier (Trans. AMS 1964)---complete deterministic ones, which we claim provide an adequate basis for the study of forward analyses as developed by Finkel and Goubault-Larrecq (Logic. Meth. Comput. Sci. 2012). Indeed, we prove that, unlike other conditions considered previously for the termination of forward analysis, boundedness is decidable. Boundedness turns out to be a valuable restriction for WSTS verification, as we show that it further allows to decide all ω\omega-regular properties on the set of infinite traces of the system

    On the central and local limit theorem for martingale difference sequences

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    Let (\Omega, \A, \mu) be a Lebesgue space and TT an ergodic measure preserving automorphism on Ω\Omega with positive entropy. We show that there is a bounded and strictly stationary martingale difference sequence defined on Ω\Omega with a common non-degenerate lattice distribution satisfying the central limit theorem with an arbitrarily slow rate of convergence and not satisfying the local limit theorem. A similar result is established for martingale difference sequences with densities provided the entropy is infinite. In addition, the martingale difference sequence may be chosen to be strongly mixing.Comment: Accepte pour publication dans Stochastics and Dynamic
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