The Kolmogorov axioms are the foundations of probability theory introduced by Andrey Kolmogorov in 1933. These axioms remain central and have direct contributions to mathematics, the physical sciences, and real-world probability cases. An alternative approach to formalising probability, favoured by some Bayesians, is given by Cox's theorem.
The assumptions as to setting up the axioms can be summarised as follows: Let (Ω, F, P) be a measure space with P being the probability of some event E, denoted , and = 1. Then (Ω, F, P) is a probability space, with sample space Ω, event space F and probability measure P.
The probability of an event is a non-negative real number:
This is the assumption of σ-additivity:
Some authors consider merely finitely additive probability spaces, in which case one just needs an algebra of sets, rather than a σ-algebra. Quasiprobability distributions in general relax the third axiom.
From the Kolmogorov axioms, one can deduce other useful rules for studying probabilities. The proofs of these rules are a very insightful procedure that illustrates the power the third axiom, and its interaction with the remaining two axioms. Four of the immediate corollaries and their proofs are shown below:
If A is a subset of, or equal to B, then the probability of A is less than, or equal to the probability of B.
Proof of monotonicity
In order to verify the monotonicity property, we set and , where and for . It is easy to see that the sets are pairwise disjoint and . Hence, we obtain from the third axiom that
Since, by the first axiom, the left-hand side of this equation is a series of non-negative numbers, and since it converges to which is finite, we obtain both and .
The probability of the empty set
In some cases, is not the only event with probability 0.
Proof of probability of the empty set
As shown in the previous proof, . However, this statement is seen by contradiction: if then the left hand side is not less than infinity;
If then we obtain a contradiction, because the sum does not exceed which is finite. Thus, . We have shown as a byproduct of the proof of monotonicity that .
The complement rule
Proof of the complement rule
Given and are mutually exclusive and that :
... (by axiom 3)
and, ... (by axiom 2)
The numeric bound
It immediately follows from the monotonicity property that
Proof of the numeric bound
Given the complement rule and axiom 1 :
Another important property is:
This is called the addition law of probability, or the sum rule. That is, the probability that A or B will happen is the sum of the probabilities that A will happen and that B will happen, minus the probability that both A and B will happen. The proof of this is as follows:
- ... (by Axiom 3)
- (by ).
and eliminating from both equations gives us the desired result.
An extension of the addition law to any number of sets is the inclusion–exclusion principle.
Setting B to the complement Ac of A in the addition law gives
That is, the probability that any event will not happen (or the event's complement) is 1 minus the probability that it will.
Simple example: coin toss
Consider a single coin-toss, and assume that the coin will either land heads (H) or tails (T) (but not both). No assumption is made as to whether the coin is fair.
We may define:
Kolmogorov's axioms imply that:
The probability of neither heads nor tails, is 0.
The probability of either heads or tails, is 1.
The sum of the probability of heads and the probability of tails, is 1.
- Borel algebra
- Set theory
- Conditional probability
- Fully probabilistic design
- Kolmogorov, Andrey (1950) . Foundations of the theory of probability. New York, USA: Chelsea Publishing Company.
- Aldous, David. "What is the significance of the Kolmogorov axioms?". David Aldous. Retrieved November 19, 2019.
- Terenin Alexander; David Draper (2015). "Cox's Theorem and the Jaynesian Interpretation of Probability". arXiv:1507.06597. Bibcode:2015arXiv150706597T. Cite journal requires
- Hájek, Alan (August 28, 2019). "Interpretations of Probability". Stanford Encyclopedia of Philosophy. Retrieved November 17, 2019.
- Ross, Sheldon M. (2014). A first course in probability (Ninth ed.). Upper Saddle River, New Jersey. pp. 27, 28. ISBN 978-0-321-79477-2. OCLC 827003384.
- Gerard, David (December 9, 2017). "Proofs from axioms" (PDF). Retrieved November 20, 2019.
- Jackson, Bill (2010). "Probability (Lecture Notes - Week 3)" (PDF). School of Mathematics, Queen Mary University of London. Retrieved November 20, 2019.
This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. (November 2010) (Learn how and when to remove this template message)
- DeGroot, Morris H. (1975). Probability and Statistics. Reading: Addison-Wesley. pp. 12–16. ISBN 0-201-01503-X.
- McCord, James R.; Moroney, Richard M. (1964). "Axiomatic Probability". Introduction to Probability Theory. New York: Macmillan. pp. 13–28.
- Formal definition of probability in the Mizar system, and the list of theorems formally proved about it.