Results 271 to 280 of about 63,200 (305)

Weak Convergence of Probability Measures

2013
Let X = (X1, X2,…, X p ) be a p-vector variable with df \( \mathbb{F} \) and dm denoted by µ X or µF. The df F j of X j is called the j th marginal of X or of \( \mathbb{F} \) or of µF, 1 ≤ j ≤ p.
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On “predictable” convergence criteria in variation of probability measures

Russian Mathematical Surveys, 1984
This is an announcement of the results published later with the proofs in the author's paper ''On necessary and sufficient conditions for convergence of probability measures in variation''. Stochastic Processes Appl. 18, 99-112 (1984; Zbl 0547.60008).
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Weak Convergence of Probability Measures

1977
Throughout this chapter we shall concern ourselves with the study of probability measures on separable metric spaces only. As usual, for any such metric space X we shall write B X for the borel σ-algebra of subsets of X. We shall denote by C(X) the space of all bounded real valued continuous functions on X and M0(X) the space of all probability ...
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FUZZY CONVERGENCE VERSUS WEAK CONVERGENCE IN SPACES OF PROBABILITY MEASURES

1984
If X is a separable metrizable space, then on the set \({\mathcal M}(X)\) of all probability measures on X, the structure most frequently used is the weak topology, also called topology of weak convergence. In Math. Nachr. 115, 33-57 (1984; Zbl 0593.54006), the author introduced an alternative structure, a fuzzy topology, the topological modification ...
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Strong measures of concordance and convergence in probability

Rivista di Matematica per le Scienze Economiche e Sociali, 1984
It is shown that convergence in probability is sufficient for a strong measure of concordance to converge to one, and that convergence to one of a strong measure of concordance, along with convergence in law, is sufficient for convergence in probability.
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On weak convergence of probability measures, channel capacity and code error probabilities

IEEE Transactions on Information Theory, 1996
Let \({\mathcal X}\) be the possibly infinite set of channel inputs and \({\mathcal Y}\) the output alphabet where \({\mathcal Y}\) is the Borel \(\sigma\)-field of a separable metric space \(({\mathcal Y},d)\). A channel \({\mathcal C}\) is a family of probability measures on \({\mathcal Y}\), i.e., \({\mathcal C}=(P^x)_{x\in{\mathcal X}}\) where \(P ...
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Convergence of Convolution Sequences of Probability Measures

1977
The study of the convergence of sequences of probability measures on a locally compact group is basic for the development of the central limit theorem. This chapter serves as a source of preparatory material as well as a presentation of the smooth part of the theory.
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Stochastic two-scale convergence and Young measures

Networks and Heterogeneous Media, 2022
Martin Heida, Stefan Neukamm
exaly  

CONVERGENCE OF ITERATES OF PROBABILITY MEASURES ON LOCALLY COMPACT GROUPS

Baku Mathematical Journal
Some related problems are also ...
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Convergence for varying measures in the topological case

Annali Di Matematica Pura Ed Applicata, 2023
L Di Piazza, Valeria Marraffa, K Musiał   +2 more
exaly