enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Convergent series - Wikipedia

    en.wikipedia.org/wiki/Convergent_series

    A series is convergent (or converges) if and only if the sequence (,,, … ) {\displaystyle (S_{1},S_{2},S_{3},\dots )} of its partial sums tends to a limit ; that means that, when adding one a k {\displaystyle a_{k}} after the other in the order given by the indices , one gets partial sums that become closer and closer to a given number.

  3. Convergence of Fourier series - Wikipedia

    en.wikipedia.org/wiki/Convergence_of_Fourier_series

    However Carleson's theorem shows that for a given continuous function the Fourier series converges almost everywhere. It is also possible to give explicit examples of a continuous function whose Fourier series diverges at 0: for instance, the even and 2π-periodic function f defined for all x in [0,π] by [9]

  4. Convergence of random variables - Wikipedia

    en.wikipedia.org/wiki/Convergence_of_random...

    The continuous mapping theorem states that for a continuous function g, if the sequence {X n} converges in distribution to X, then {g(X n)} converges in distribution to g(X). Note however that convergence in distribution of {X n} to X and {Y n} to Y does in general not imply convergence in distribution of {X n + Y n} to X + Y or of {X n Y n} to XY.

  5. Convergence tests - Wikipedia

    en.wikipedia.org/wiki/Convergence_tests

    If r < 1, then the series converges absolutely. If r > 1, then the series diverges. If r = 1, the root test is inconclusive, and the series may converge or diverge. The root test is stronger than the ratio test: whenever the ratio test determines the convergence or divergence of an infinite series, the root test does too, but not conversely. [1]

  6. Proofs of convergence of random variables - Wikipedia

    en.wikipedia.org/wiki/Proofs_of_convergence_of...

    First we want to show that (X n, c) converges in distribution to (X, c). By the portmanteau lemma this will be true if we can show that E[f(X n, c)] → E[f(X, c)] for any bounded continuous function f(x, y). So let f be such arbitrary bounded continuous function. Now consider the function of a single variable g(x) := f(x, c).

  7. Direct comparison test - Wikipedia

    en.wikipedia.org/wiki/Direct_comparison_test

    In mathematics, the comparison test, sometimes called the direct comparison test to distinguish it from similar related tests (especially the limit comparison test), provides a way of deducing whether an infinite series or an improper integral converges or diverges by comparing the series or integral to one whose convergence properties are known.

  8. Dirichlet's test - Wikipedia

    en.wikipedia.org/wiki/Dirichlet's_test

    If the integral of a function f is uniformly bounded over all intervals, and g is a non-negative monotonically decreasing function, then the integral of fg is a convergent improper integral. Notes [ edit ]

  9. Limit comparison test - Wikipedia

    en.wikipedia.org/wiki/Limit_comparison_test

    If diverges and converges, then necessarily =, that is, =. The essential content here is that in some sense the numbers a n {\displaystyle a_{n}} are larger than the numbers b n {\displaystyle b_{n}} .