Search results
Results from the WOW.Com Content Network
As an example one may consider random variables with densities f n (x) = (1 + cos(2πnx))1 (0,1). These random variables converge in distribution to a uniform U(0, 1), whereas their densities do not converge at all. [3] However, according to Scheffé’s theorem, convergence of the probability density functions implies convergence in ...
In probability theory, Slutsky's theorem extends some properties of algebraic operations on convergent sequences of real numbers to sequences of random variables. [1] The theorem was named after Eugen Slutsky. [2] Slutsky's theorem is also attributed to Harald Cramér. [3]
The plot of a convergent sequence (a n) is shown in blue. From the graph we can see that the sequence is converging to the limit zero as n increases. An important property of a sequence is convergence. If a sequence converges, it converges to a particular value known as the limit. If a sequence converges to some limit, then it is convergent.
A sequence that converges to is called a null sequence and is said to vanish. The set of all sequences that converge to is a closed vector subspace of that when endowed with the supremum norm becomes a Banach space that is denoted by and is called the space of null sequences or the space of vanishing sequences.
Each of the probabilities on the right-hand side converge to zero as n → ∞ by definition of the convergence of {X n} and {Y n} in probability to X and Y respectively. Taking the limit we conclude that the left-hand side also converges to zero, and therefore the sequence {(X n, Y n)} converges in probability to {(X, Y)}.
Here, one can see that the sequence is converging to the limit 0 as n increases. In the real numbers , a number L {\displaystyle L} is the limit of the sequence ( x n ) {\displaystyle (x_{n})} , if the numbers in the sequence become closer and closer to L {\displaystyle L} , and not to any other number.
In the case that a = 0, the series is also called a Maclaurin series. A Taylor series of f about point a may diverge, converge at only the point a, converge for all x such that | | < (the largest such R for which convergence is guaranteed is called the radius of convergence), or converge on the entire real line. Even a converging Taylor series ...
Using the concept of the impossibility of a gambling system, von Mises defined an infinite sequence of zeros and ones as random if it is not biased by having the frequency stability property i.e. the frequency of zeros goes to 1/2 and every sub-sequence we can select from it by a "proper" method of selection is also not biased.