Markov defined a way to represent real-world stochastic systems and processes that encode dependencies and reach a steady-state over time.

Andrei Markov didn’t agree with Pavel Nebrasov, when he said independence between variables was necessary for the Weak Law of Large Numbers to be applied.

The Weak Law of Large Numbers states something like this:

When you collect independent samples, as the number of samples gets bigger, the mean of those samples converges to the true mean of the population.

But Markov believed independence was not a necessary condition for the mean to converge. So he set out to define how the average of the outcomes from a process involving dependent random variables could converge over time. Read More