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Basic Probability Formulas
Complementary events: The complement of event A is everything not in A. Complementary events are mutually
exclusive events and together make up the sample space. The probability of the sample space is one.
Independent events: The occurrence of any one of the events does not affect the probabilities of the occurrences
of the other events. Events A and B are independent if probability of A given B equals probability of A.
Dependent events (or non-independent events): Events that are not independent, i.e., P(A given B) P(A).
Mutually exclusive events (or disjoint events): If event A occurs, then event B cannot occur, and conversely.
De Morgan's Rule (one form): Via a double complement, A or B = (Ac and Bc)c = "not [ (not A) and (not B) ]". For
example, "I want A, B, or both to work" (Reliability) equates to "I do not want both A and B not to work" (Safety).
Event Details Formula (from English to mathematical operations)
A Probability of A, P(A) P(A) is at or between zero and one: 0 P(A) 1
not A, Ac Ac is the complement of A Probability of not A = P(Ac) = 1 - P(A)
A and B are independent
events P(A and B) = P(A)*P(B)
A and B A and B are dependent
events P(A and B) = P(A)*P(B | A) = P(B)*P(A | B) as 2 forms
A and B are mutually
exclusive events P(A and B) = 0
A and B are independent P(A or B) = P(A) + P(B) - P(A)*P(B) conveniently expands to
events = 1 - [1 - P(A)]*[1 - P(B)] or is obtained from De Morgan's Rule
A or B A and B are dependent
events P(A or B) = P(A) + P(B) - P(A)*P(B | A) as 1 of 2 forms
A and B are mutually
exclusive events P(A or B) = P(A) + P(B)
A given B, Conditional: outcome of A P(A given B) = P(A | B) = P(A)*P(B | A) / P(B) [Bayes' Thm]
A | B given B has occurred To make this formula, solve the 2 forms in "A and B" for P(A | B)
What does probability space mean?In short, a probability space is a measure space such that the measure of the whole space is equal to one. . . Probabilities can be ascribed to points of . All subsets of
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