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ECN 221 > Chapter 4: Introduction to Probability > Flashcards

Chapter 4: Introduction to Probability Flashcards

1
Q

addition law

A

A probability law used to compute the probability of the union of two events. It is P(A⋂B) = P(A) +P(B) - P(A⋃B). For mutually exclusive events, P(A⋂B) = 0; in this case the addtion law reduces to P(A⋃B) = P(A) + P(B).

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2
Q

A probability law used to compute the probability of the union of two events. It is P(A⋂B) = P(A) +P(B) - P(A⋃B). For mutually exclusive events, P(A⋂B) = 0; in this case the addtion law reduces to P(A⋃B) = P(A) + P(B).

A

addition law

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3
Q

basic requirements for assigning probabilities

A

Two requirements that restrict the manner in which probability assignments can be made: (1) for each experimental outcome E∨i we must have 0 <= P(E∨i) <= 1; (2) considering all experimental outcomes, we must have P(E∨1) + P(E∨2) + … + P(E∨n) = 1.0

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4
Q

Two requirements that restrict the manner in which probability assignments can be made: (1) for each experimental outcome E∨i we must have 0 <= P(E∨i) <= 1; (2) considering all experimental outcomes, we must have P(E∨1) + P(E∨2) + … + P(E∨n) = 1.0

A

basic requirements for assigning probabilities

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5
Q

Bayes theorem

A

A method used to compute posterior probabilities.

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6
Q

A method used to compute posterior probabilities.

A

Bayes theorem

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7
Q

classical method

A

A method of assigning probabilities that is appropriate when all the experimental outcomes are equally likely.

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8
Q

A method of assigning probabilities that is appropriate when all the experimental outcomes are equally likely.

A

classical method

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9
Q

complement of A

A

The event consisting of all sample points that are not in A.

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10
Q

The event consisting of all sample points that are not in A.

A

complement of A

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11
Q

event

A

A collection of sample points.

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12
Q

A collection of sample points.

A

event

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13
Q

experiment

A

A process that generates well-defined outcomes.

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14
Q

A process that generates well-defined outcomes.

A

experiment

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15
Q

independent events

A

Two events A and B where P(A⎮B) = P(A) or P(B⎮A) = P(B); that is, the events have no influence on each other.

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16
Q

Two events A and B where P(A⎮B) = P(A) or P(B⎮A) = P(B); that is, the events have no influence on each other.

A

independent events

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17
Q

intersection of A and B

A

The event containing the sample points belonging to both A and B. The intersection is denoted A⋂B.

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18
Q

The event containing the sample points belonging to both A and B. The intersection is denoted A⋂B.

A

intersection of A and B

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19
Q

joint probability

A

The probability of two events both occurring; that is, the probability of the intersection of two events.

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20
Q

The probability of two events both occurring; that is, the probability of the intersection of two events.

A

joint probability

21
Q

marginal probability

A

The values in the margins of a joint probability table that provide the probabilities of each event separately.

22
Q

The values in the margins of a joint probability table that provide the probabilities of each event separately.

A

marginal probability

23
Q

multiple-step experiment

A

An experiment that can be described as a sequence of steps. If a multiple-step experiment has k steps with n∨1 possible outcomes on the first step, n∨2 possible outcomes on the second step, and so on, the total number of experimental outcomes is (n∨1)(n∨2)…(n∨k).

24
Q

An experiment that can be described as a sequence of steps. If a multiple-step experiment has k steps with n∨1 possible outcomes on the first step, n∨2 possible outcomes on the second step, and so on, the total number of experimental outcomes is (n∨1)(n∨2)…(n∨k).

A

multiple-step experiment

25
Q

multiplication law

A

A probability law used to compute the probability of the intersection of two events. It is P(A⋂B) = P(B)P(A⎮B) or P(A⋂B) = P(A)P(B⎮A). For independent events it reduces to P(A⋂B) = P(A)P(B).

26
Q

A probability law used to compute the probability of the intersection of two events. It is P(A⋂B) = P(B)P(A⎮B) or P(A⋂B) = P(A)P(B⎮A). For independent events it reduces to P(A⋂B) = P(A)P(B).

A

multiplication law

27
Q

mutually exclusive events

A

Events that have no sample points in common; that is A⋂B is empty and P(A⋂B) = 0.

28
Q

Events that have no sample points in common; that is A⋂B is empty and P(A⋂B) = 0.

A

mutually exclusive events

29
Q

posterior probabilities

A

Revised probabilities of events based on additional information.

30
Q

Revised probabilities of events based on additional information.

A

posterior probabilities

31
Q

prior probabilities

A

Initial estimates of the probabilities of events.

32
Q

Initial estimates of the probabilities of events.

A

prior probabilities

33
Q

probability

A

A numerical measure of the likelihood that an event will occur.

34
Q

A numerical measure of the likelihood that an event will occur.

A

probability

35
Q

relative frequency

A

A method of assigning probabilities that is appropriate when data are available to estimate the proportion of the time the experimental outcome will occur if the experiment is repeated a large number of times.

36
Q

A method of assigning probabilities that is appropriate when data are available to estimate the proportion of the time the experimental outcome will occur if the experiment is repeated a large number of times.

A

relative frequency

37
Q

sample point

A

An element of the sample space. A sample point represents an experimental outcome.

38
Q

An element of the sample space. Represents an experimental outcome.

A

sample point

39
Q

sample space

A

The set of all experimental outcomes.

40
Q

The set of all experimental outcomes.

A

sample space

41
Q

subjective method

A

A method of assigning probabilities on the basis of judgment.

42
Q

A method of assigning probabilities on the basis of judgment.

A

subjective method

43
Q

tree diagram

A

A graphical representation that helps in visualizing a multiple-step experiment.

44
Q

A graphical representation that helps in visualizing a multiple-step experiment.

A

tree diagram

45
Q

union of A and B

A

The event containing all sample points belonging to A or B or both. The union is denoted A⋃B.

46
Q

The event containing all sample points belonging to A or B or both. The union is denoted A⋃B.

A

union of A and B

47
Q

Venn diagram

A

A graphical representation for showing symbolically the sample space and operations involving events in which the sample space is represented by a rectangle and events are represented as circles within the sample space.

48
Q

A graphical representation for showing symbolically the sample space and operations involving events in which the sample space is represented by a rectangle and events are represented as circles within the sample space.

A

Venn diagram

49
Q
A

ECN 221 (6 decks)

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