Axiomatic Probability Example

One key aspect of probability is that it can only be applied to trials in which the complete number of outcomes is known, i.e., the idea of probability cannot be applied unless and until the total number of outcomes is known.

To apply probability in everyday settings, we must first understand the entire number of alternative outcomes of the experiment. Axiomatic Probability is a different approach to expressing the likelihood of an event. As the name implies, several axioms are predefined before assigning probabilities in this technique. This is done to quantify the event and so make calculating the event’s occurrence or non-occurrence easier.

• In mathematics, axiomatic probability is a theory that unifies probability.
• The axiomatic approach to probability establishes a set of assumptions that apply to all probabilistic approaches, including frequentist and classical probabilistic approaches.
• The Three Axioms of Kolmogorov form the basis for these principles.
• Mathematical probability begins with axiomatic probability.

Three axioms of kolmogorov’s:

Andrey Nikolaevich Kolmogorov, a Russian mathematician who lived from 1903 to 1987, pioneered the axiomatic approach to probability. According to him, there are three axioms that can be used to calculate the likelihood of any event (E). The Three Axioms of Kolmogorov are as follows:

The first axiom:

1. Axiomatic probability’s first axiom specifies that any event’s probability must fall between 0 and 1.
2. The number 0 indicates that the event will never occur, whereas 1 indicates that it will occur.
3. Any event cannot have a negative probability. The probability of any event P (A) has the minimum value of zero, and if probability P (A) =0, event A will never occur.

The second axiom:

1. The axiomatic probability of the entire sample space has one as its second axiom (100 percent).
2. This is because the sample space S contains all possible results of our random experiment, or something happens if the experiment is run at any time. As a result, each trial’s result is always part of experiment S’s sample space.
3. As a result, event S occurs every time, and P(S) =1.

The third axiom:

1. The most intriguing postulate of probability is the third.
2. The underlying principle behind this axiom is that if any of the events are disjoint (that is, there is no overlap between them), the probability of their union must be equal to the sum of their probabilities.

Example of axiomatic probability:

1) There are four candidates in a presidential election. A, B, C, and D are the four candidates. According to polling data, candidate A has a 20% chance of winning this election, whereas candidate B has a 40% chance of winning. What are the chances of candidate A or B winning the election?

Solution: We can see that the events A wins the election, B wins the election, C wins the election, and D wins the election are discontinuous events since more than one of them cannot happen at the same time. If candidate A wins, for example, candidate B cannot win the election. We know that the third probability axiom states that,

If A and B are mutually exclusive, then P (A1 ∪ A2) = P (A1) + P (A2).

As a result, Probability P (A wins election or B wins election) = P {(A wins election) ∪ (B wins election)} = P (A wins election) + P (B wins election).

=P ({A wins election}) +P ({B wins election})

=(20/100)+(40/100)

=0.2+0.4

= 0.6

As a result, there is a 0.6 chance that candidate A or candidate B will win the election.

Application of axiomatic probability:

• Modeling and risk assessment applications. This is how markets and insurance firms determine price and make decisions.
• It can be used to assess trends in biology and ecology.
• We can use probability to build games based on player feedback and references from previous games.

Conclusion:

Theoretical probability is based on the assumption of a perfect condition. Because a flipped coin has two sides and each side has an equal chance of landing up, the theoretical probability of landing heads (or tails) is exactly one out of two. One key aspect of probability is that it can only be applied to trials in which the complete number of outcomes is known. 

Axiomatic Probability is a different approach to expressing the likelihood of an event. As the name implies, several axioms are predefined before assigning probabilities in this technique.

Andrey Nikolaevich Kolmogorov, a Russian mathematician who lived from 1903 to 1987, pioneered the axiomatic approach to probability. According to him, there are three axioms that can be used to calculate the likelihood of any event (E).

Modeling and risk assessment applications. This is how markets and insurance firms determine price and make decisions. It can be used to assess trends in biology and ecology. Also We can use probability to build games based on player feedback and references from previous games.