Stochastic convergence

Stochastic convergence is a mathematical concept intended to formalize the idea that a sequence of essentially random or unpredictable events sometimes tends to settle into a pattern.

Four different varieties of stochastic convergence are noted:

• Almost sure convergence
• Convergence in probability
• Convergence in distribution
• Convergence in nth order mean

Almost sure convergence

Example

Consider a short-lived animal of some species. We may note the exact amount of food the animal consumes day by day. This sequence of numbers will be unpredictable in advance, but we may be quite certain that one day the number will be zero, and stay zero forever after.

Formal definition

Let $X_{0},X_{1},...$ be a sequence of stochastic variables.

If $P(\lim _{i\to \infty }X_{i}=a)=1$ for some a, then the sequence has almost sure convergence to a.

Convergence in probability

Example

We may keep tossing a die an infinite number of times and at every toss note the average outcome so far. The exact number thus obtained after each toss will be unpredictable, but for a fair die, it will tend to get closer and closer to the arithmetic average of 1,2,3,4,5 and 6, i.e. 3.5.

Relations between the different modes of convergence

• If a stochastic sequence has almost sure convergence, then it also has convergence in probability.
• If a stochastic sequence has convergence in probability, then it also has convergence in distribution.
• If a stochastic sequence has convergence in (n+1)th order mean, then it also has convergence in nth order mean (n>0).
• If a stochastic sequence has convergence in nth order mean, then it also has convergence in probability.

See also

• Stochastic differential equations

Related topics

• Probability
• Probability theory
• Stochastic variable
• Differential equations
• Stochastic modeling