spaces in vector lattices and applications
arXiv:1604.07570 · doi:10.1515/ms-2017-0060
Abstract
spaces are investigated for vector lattice-valued functions, with respect to filter convergence. As applications, some classical inequalities are extended to the vector lattice context, and some properties of the Brownian Motion and the Brownian Bridge are studied, to solve some stochastic differential equations.
21 pages, 2 figures
References in corpus (1)
Cited by in corpus (8)
- Convergence in Orlicz spaces by means of the multivariate max-product neural network operators of the Kantorovich type and applications
- Approximation results in Orlicz spaces for sequences of Kantorovich max-product neural network operators
- Kuelbs-Steadman spaces for Banach space-valued measures
- Abstract integration with respect to measures and applications to modular convergence in vector lattice setting
- Set-valued Brownian motion
- The Hájek-Rényi-Chow maximal inequality and a strong law of large numbers in Riesz spaces
- Some applications of modular convergence in vector lattice setting
- A residue theorem for polar analytic functions and Mellin analogues of Boas' differentiation formula and Valiron's sampling formula