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  2. 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]

  3. Rate of convergence - Wikipedia

    en.wikipedia.org/wiki/Rate_of_convergence

    In asymptotic analysis in general, one sequence () that converges to a limit is said to asymptotically converge to with a faster order of convergence than another sequence () that converges to in a shared metric space with distance metric | |, such as the real numbers or complex numbers with the ordinary absolute difference metrics, if

  4. 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).

  5. 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.

  6. Convergence proof techniques - Wikipedia

    en.wikipedia.org/wiki/Convergence_proof_techniques

    Convergence proof techniques are canonical patterns of mathematical proofs that sequences or functions converge to a finite limit when the argument tends to infinity.. There are many types of sequences and modes of convergence, and different proof techniques may be more appropriate than others for proving each type of convergence of each type of sequence.

  7. 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 ]

  8. Uniform convergence - Wikipedia

    en.wikipedia.org/wiki/Uniform_convergence

    A sequence of functions () converges uniformly to when for arbitrary small there is an index such that the graph of is in the -tube around f whenever . The limit of a sequence of continuous functions does not have to be continuous: the sequence of functions () = ⁡ (marked in green and blue) converges pointwise over the entire domain, but the limit function is discontinuous (marked in red).

  9. Pointwise convergence - Wikipedia

    en.wikipedia.org/wiki/Pointwise_convergence

    This concept is often contrasted with uniform convergence.To say that = means that {| () |:} =, where is the common domain of and , and stands for the supremum.That is a stronger statement than the assertion of pointwise convergence: every uniformly convergent sequence is pointwise convergent, to the same limiting function, but some pointwise convergent sequences are not uniformly convergent.

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