Search results
Results From The WOW.Com Content Network
The most important cases of convergence in r-th mean are: When X n converges in r-th mean to X for r = 1, we say that X n converges in mean to X. When X n converges in r-th mean to X for r = 2, we say that X n converges in mean square (or in quadratic mean) to X. Convergence in the r-th mean, for r ≥ 1, implies convergence in probability (by ...
In mathematics, strong convergence may refer to: The strong convergence of random variables of a probability distribution . The norm-convergence of a sequence in a Hilbert space (as opposed to weak convergence ).
For (,) a measurable space, a sequence μ n is said to converge setwise to a limit μ if = ()for every set .. Typical arrow notations are and .. For example, as a consequence of the Riemann–Lebesgue lemma, the sequence μ n of measures on the interval [−1, 1] given by μ n (dx) = (1 + sin(nx))dx converges setwise to Lebesgue measure, but it does not converge in total variation.
In mathematics, weak convergence may refer to: Weak convergence of random variables of a probability distribution; Weak convergence of measures, of a sequence of probability measures; Weak convergence (Hilbert space) of a sequence in a Hilbert space more generally, convergence in weak topology in a Banach space or a topological vector space
The definition of weak convergence can be extended to Banach spaces. A sequence of points ( x n ) {\displaystyle (x_{n})} in a Banach space B is said to converge weakly to a point x in B if f ( x n ) → f ( x ) {\displaystyle f(x_{n})\to f(x)} for any bounded linear functional f {\displaystyle f} defined on B {\displaystyle B} , that is, for ...
In analysis, a topology is called strong if it has many open sets and weak if it has few open sets, so that the corresponding modes of convergence are, respectively, strong and weak. (In topology proper, these terms can suggest the opposite meaning, so strong and weak are replaced with, respectively, fine and coarse.)
The fixed-point problem can be solved with fixed-point iterations, also called (block) Gauss–Seidel iterations, [21] which means that the flow problem and structural problem are solved successively until the change is smaller than the convergence criterion.
However, when the convergence is strong enough, then new evidence inconsistent with the previous conclusion is not usually enough to outweigh that convergence. Without an equally strong convergence on the new result, the weight of evidence will still favor the established result. This means that the new evidence is most likely to be wrong.