Convergence of random variables

In probability theory, there exist several different notions of convergence of random variables. The convergence of sequences of random variables to some limit random variable is an important concept in probability theory, and its applications to statistics and stochastic processes. The same concepts are known in more general mathematics as stochastic convergence and they formalize the idea that a sequence of essentially random or unpredictable events can sometimes be expected to settle down into a behavior that is essentially unchanging when items far enough into the sequence are studied. The different possible notions of convergence relate to how such a behavior can be characterized: two readily understood behaviors are that the sequence eventually takes a constant value, and that values in the sequence continue to change but can be described by an unchanging probability distribution. "Stochastic convergence" formalizes the idea that a sequence of essentially random or unpredictable events can sometimes be expected to settle into a pattern. The pattern may for instance be Convergence in the classical sense to a fixed value, perhaps itself coming from a random event An increasing similarity of outcomes to what a purely deterministic function would produce An increasing preference towards a certain outcome An increasing "aversion" against straying far away from a certain outcome That the probability distribution describing the next outcome may grow increasingly similar to a certain distribution Some less obvious, more theoretical patterns could be That the series formed by calculating the expected value of the outcome's distance from a particular value may converge to 0 That the variance of the random variable describing the next event grows smaller and smaller. These other types of patterns that may arise are reflected in the different types of stochastic convergence that have been studied.