Preliminaries Flashcards

1
Q

Define a sample space.

A

A sample space Ω is a collection of all possible outcomes of a probabilistic experiment.

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2
Q

What symbol represents a sample space?

A

Ω

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3
Q

Define an event.

A

An event is a collection of possible outcomes.

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4
Q

What symbol represents the impossible event?

A

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5
Q

What symbol represents a certain event?

A

Ω

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6
Q

Define a field.

A
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7
Q

Are fields open or closed w.r.t. taking finite unions or intersections?

A

Closed

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8
Q

Define a σ-field.

A
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9
Q

What can you replace property 2 by in the following?

A
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10
Q

What is the smallest σ-field in Ω?

A

{∅,Ω}

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11
Q

What is the biggest σ-field in Ω?

A

All the subsets of Ω.

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12
Q

Define a probability distribution.

A
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13
Q

What are the A1-A4 properties of a probability distribution?

A
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14
Q

What are the P1-P3 properties that following on from the following properties?

A
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15
Q

Define a probability space.

A
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16
Q

What is another name for a probabiluty space?

A

Probability measure.

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17
Q

What is the pair (Ω,𝑭) called?

A

Measurable space

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18
Q

Finish the following lemma.

A
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19
Q

Prove the following Lemma

A

Need to take photo.

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20
Q

Define conditional probability.

A
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21
Q

What is P4 - the multiplication rule for probabilities?

A
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22
Q

What is P5 - partition theorem or formula of total probabilities?

A
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23
Q

What is P6 - Bayes’ theorem?

A
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24
Q

Define independent.

A
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25
Q

Define mutually independent.

A
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26
Q

Define a random variable, X.

A

If the sample space of possible outcomes is a set of real outcomes, then the outcome to the probabilistic experiment is called a random variable.

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27
Q

What is the probability distribution of a r.v. X?

A

The collection of probabilites ℙ(X ∈ A) for all intervals A ⊆ ℝ.

28
Q

When is X a discrete r.v.?

A

If in addition Ω is countable, i.e. if the possible values for X can be enumerated in a (possiby infinite) list.

29
Q

What is the probability mass function of a discrete r.v. X?

30
Q

What is the probability distribution when X is a discrete r.v.?

31
Q

What is the probability distribution for a continuous r.v. X?

32
Q

Define the cumulative distribution function.

33
Q

Define a multivariate random variable.

34
Q

What is the joint probability distribution of (X,Y)?

35
Q

What is the marginal probability distribution of X in a joint distribution?

36
Q

What is the probability mass function for the conditional distribution of X given Y?

37
Q

What is the r.v. version of the partion theorem called?

A

Law of total probability.

38
Q

What is the law of total probability?

39
Q

If X and Y are independent what does p(x,y) factorise to?

40
Q

Define when two random variable X, Y are independent.

41
Q

What is the continuous probability density function for a joint distribution of (X,Y)?

42
Q

What are the marginal pdfs for the joint, continuous r.v. (X,Y)?

43
Q

What is the continuous conditional density distribution of X given Y?

44
Q

Define the expected value.

45
Q

What are the names of the four important properties of expectation?

A
  1. Linearity
  2. Monotonicity
  3. Multivariate linearity
  4. Independence
46
Q

What is the equation for variance?

47
Q

Define conditional expectation.

48
Q

What is the E1 - linearity - property of expectation?

49
Q

What is the formula for covariance?

50
Q

What does covariance equal to show two varaibles are uncorrelated?

51
Q

What does the following equal?

52
Q

What does the following equal when the variables are pairwise uncorrelated?

53
Q

What is the partition theorm for expectation?

54
Q

What is the law of large numbers theorem?

55
Q

What is the central limit theorem?

56
Q

Define the moment generating function.

57
Q

What are the name of the five useful properties of the moment generating function?

A
  1. Expectation
  2. Uniqueness
  3. Linear transformation
  4. Independence
  5. Convergence
58
Q

What is the M1 - expectation - property of moment generating functions?

59
Q

What is the M2 - uniqueness - property of moment generating functions?

60
Q

What is the M3 - linear transformation - property of moment generating functions?

61
Q

What is the M4 - independence - property of moment generating functions?

62
Q

What is the M5 - convergence - property of moment generating functions?

63
Q

What is the E2 - monotonicity - property of expectation?

64
Q

What is the E3 - multivaraite independence - property of expectation?

65
Q

What is the E4 - independence - property of expectation?