Convergence of random variables Created 2026-09-24 Updated 2026-10-14
The main modes include almost sure convergence, convergence in probability, convergence in Lp, and convergence in distribution. The strong law of large numbers and weak law of large numbers explain why a sample mean approaches an expected value; the central limit theorem describes the probability distribution of its remaining error after magnification by .
For deterministic square-integrable and a Poisson random measure of intensity , first define the integral for simple functions on disjoint finite-intensity sets. Independence and the equality of Poisson distribution mean and variance prove the displayed isometry. Completion defines the integral for all , even when . A small-jump compensated martingale is thereby defined through convergence in Lp, not by subtracting two divergent ordinary integrals.
Lp martingale convergence theorem Created 2026-10-05 Updated 2026-10-14
A martingale satisfying , for , has almost sure convergence and convergence in Lp to a limit . Also . The Doob Lp maximal inequality makes integrable to the power ; the martingale convergence theorem gives the almost-sure limit, and the dominated convergence theorem gives convergence in the Lp norm. Boundedness in alone does not imply convergence in L1.
The uniformly integrable martingale convergence theorem states that a uniformly integrable martingale has an integrable terminal random variable such that almost surely and in the L1 norm, andThe last identity follows by passing to the limit of a sequence in for and , using convergence in L1.
Part (b)(iii) verifies uniform integrability for , soMoreover almost surely. In this particular problem the common bound also yields convergence in Lp for every finite , including convergence in L2: the dominated convergence theorem applies to . General uniform integrability alone does not assert these higher-power conclusions.