Editing Convergence of random variables (section)

{{Short description|Notions of probabilistic convergence, applied to estimation and asymptotic analysis}}
In [[probability theory]], there exist several different notions of '''convergence of sequences of random variables''', including ''convergence in probability'', ''convergence in distribution'', and ''almost sure convergence''. The different notions of convergence capture different properties about the sequence, with some notions of convergence being stronger than others. For example, convergence in distribution tells us about the limit [[Probability distribution|distribution]] of a sequence of random variables. This is a weaker notion than convergence in probability, which tells us about the value a random variable will take, rather than just the distribution.

The concept is important in probability theory, and its applications to [[statistics]] and [[stochastic process]]es. The same concepts are known in more general [[mathematics]] as '''stochastic convergence''' and they formalize the idea that certain properties of 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.