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Statistics for Machine Learning > 5. Bayesian Statistics > Flashcards

5. Bayesian Statistics Flashcards

Principles of Bayesian Statistics for Machine Learning

1
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

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2
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

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3
Q

What is a prior probability?

A

An initial probability before observing new data.

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4
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

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5
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

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6
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

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7
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

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8
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

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9
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

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10
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

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11
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

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12
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

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13
Q

What is a prior probability?

A

An initial probability before observing new data.

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14
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

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15
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

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16
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

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17
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

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18
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

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19
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

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20
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

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21
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

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3
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22
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

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3
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23
Q

What is a prior probability?

A

An initial probability before observing new data.

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3
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5
Perfectly
24
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

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3
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5
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25
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

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26
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

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27
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

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28
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

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29
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

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30
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

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3
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31
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

How well did you know this?
1
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2
3
4
5
Perfectly
32
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

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1
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3
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33
Q

What is a prior probability?

A

An initial probability before observing new data.

How well did you know this?
1
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2
3
4
5
Perfectly
34
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

How well did you know this?
1
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2
3
4
5
Perfectly
35
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

How well did you know this?
1
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2
3
4
5
Perfectly
36
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

How well did you know this?
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3
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37
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

How well did you know this?
1
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2
3
4
5
Perfectly
38
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

How well did you know this?
1
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3
4
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39
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

How well did you know this?
1
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2
3
4
5
Perfectly
40
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
41
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

42
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

43
Q

What is a prior probability?

A

An initial probability before observing new data.

44
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

45
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

46
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

47
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

48
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

49
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

50
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

51
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

52
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

53
Q

What is a prior probability?

A

An initial probability before observing new data.

54
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

55
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

56
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

57
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

58
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

59
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

60
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

61
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

62
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

63
Q

What is a prior probability?

A

An initial probability before observing new data.

64
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

65
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

66
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

67
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

68
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

69
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

70
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

71
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

72
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

73
Q

What is a prior probability?

A

An initial probability before observing new data.

74
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

75
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

76
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

77
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

78
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

79
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

80
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

81
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

82
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

83
Q

What is a prior probability?

A

An initial probability before observing new data.

84
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

85
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

86
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

87
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

88
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

89
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

90
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

91
Q

What is Bayesian inference?

A

A method of updating beliefs based on prior knowledge and new evidence.

92
Q

What is Bayes’ Theorem?

A

P(A|B) = [P(B|A) * P(A)] / P(B).

93
Q

What is a prior probability?

A

An initial probability before observing new data.

94
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

95
Q

What is a posterior probability?

A

An updated probability after considering new evidence.

96
Q

What is Maximum A Posteriori (MAP) estimation?

A

A Bayesian method of estimating parameters by maximizing the posterior distribution.

97
Q

What is a conjugate prior?

A

A prior distribution that results in a posterior distribution of the same family.

98
Q

What is the Markov Chain Monte Carlo (MCMC) method?

A

A computational technique used to approximate complex probability distributions.

99
Q

What is the difference between Frequentist and Bayesian statistics?

A

Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.

100
Q

What is Bayesian regression?

A

A form of regression that incorporates prior distributions on parameters.

Statistics for Machine Learning (9 decks)

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