Kolmogorov extension theorem
In mathematics, the Kolmogorov extension theorem or Daniell-Kolmogorov extension theorem (also known as Kolmogorov existence theorem or Kolmogorov consistency theorem) is a theorem that guarantees that a suitably "consistent" collection of finite-dimensional distributions will define a stochastic process. It is credited to the Soviet mathematician Andrey Nikolaevich Kolmogorov and also to British mathematician Percy John Daniell who discovered it independently in the slightly different setting of integration theory. 
Statement of the theorem
1. for all permutations of and measurable sets ,
2. for all measurable sets ,
Then there exists a probability space and a stochastic process such that
for all , and measurable sets , i.e. has as its finite-dimensional distributions relative to times .
In fact, it is always possible to take as the underlying probability space and to take for the canonical process . Therefore, an alternative way of stating Kolomogorov's extension theorem is that, provided that the above consistency conditions hold, there exists a (unique) measure on with marginals for any finite collection of times . Kolmogorov's extension theorem applies when is uncountable, but the price to pay for this level of generality is that the measure is only defined on the product σ-algebra of , which is not very rich.
Explanation of the conditions
The two conditions required by the theorem are trivially satisfied by any stochastic process. For example, consider a real-valued discrete-time stochastic process . Then the probability can be computed either as or as . Hence, for the finite-dimensional distributions to be consistent, it must hold that . The first condition generalises this obvious statement to hold for any number of time points , and any control sets .
Continuing the example, the second condition implies that . Also this is a trivial condition that will be satisfied by any consistent family of finite-dimensional distributions.
Implications of the theorem
Since the two conditions are trivially satisfied for any stochastic process, the powerful statement of the theorem is that no other conditions are required: For any reasonable (i.e., consistent) family of finite-dimensional distributions, there exists a stochastic process with these distributions.
The measure-theoretic approach to stochastic processes starts with a probability space and defines a stochastic process as a family of functions on this probability space. However, in many applications the starting point is really the finite-dimensional distributions of the stochastic process. The theorem says that provided the finite-dimensional distributions satisfy the obvious consistency requirements, one can always identify a probability space to match the purpose. In many situations, this means that one does not have to be explicit about what the probability space is. Many texts on stochastic processes do, indeed, assume a probability space but never state explicitly what it is.
Aldrich, J. (2007) "But you have to remember P.J.Daniell of Sheffield" Electronic Journ@l for History of Probability and Statistics December 2007.