What is PROBABILITY?

Probability is the branch of mathematics that studies the possible outcomes of given events together with the outcomes’ relative likelihoods and distributions. In common usage, the word ā€œprobabilityā€ is used to mean the chance that a particular event (or set of events) will occur expressed on a linear scale from 0 (impossibility) to 1 (certainty), also expressed as a percentage between 0 and 100%. The analysis of events governed by probability is called statistics.
There are several competing interpretations of the actual ā€œmeaningā€ of probabilities. Frequentists view probability simply as a measure of the frequency of outcomes (the more conventional interpretation), while Bayesians treat probability more subjectively as a statistical procedure that endeavors to estimate parameters of an underlying distribution based on the observed distribution.
A properly normalized function that assigns a probability ā€œdensityā€ to each possible outcome within some interval is called a probability density function (or probability distribution function), and its cumulative value (integral for a continuous distribution or sum for a discrete distribution) is called a distribution function (or cumulative distribution function).
variate is defined as the set of all random variables that obey a given probabilistic law. It is common practice to denote a variate with a capital letter (most commonly X). The set of all values that X can take is then called the range, denoted R_X (Evans et al. 2000, p. 5). Specific elements in the range of X are called quantiles and denoted x, and the probability that a variate X assumes the element x is denoted P(X=x).
Probabilities are defined to obey certain assumptions, called the probability axioms. Let a sample space contain the union ( union ) of all possible events E_i, so
 S=( union _(i=1)^NE_i),
http://and let E and F denote subsets of S. Further, let F^'=not-F be the complement of F, so that
 F union F^'=S.
Then the set E can be written as
 E=E intersection S=E intersection (F union F^')=(E intersection F) union (E intersection F^'),
where  intersection  denotes the intersection. Then
P(E)=P(E intersection F)+P(E intersection F^')-P[(E intersection F) intersection (E intersection F^')]
=P(E intersection F)+P(E intersection F^')-P[(F intersection F^') intersection (E intersection E)]
=P(E intersection F)+P(E intersection F^')-P(emptyset intersection E)
=P(E intersection F)+P(E intersection F^')-P(emptyset)
=P(E intersection F)+P(E intersection F^'),
where emptyset is the empty set.

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