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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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22
Q

What is Bayes’ Theorem?

A

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

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23
Q

What is a prior probability?

A

An initial probability before observing new data.

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24
Q

What is a likelihood function?

A

The probability of observed data given a parameter value.

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25
What is a posterior probability?
An updated probability after considering new evidence.
26
What is Maximum A Posteriori (MAP) estimation?
A Bayesian method of estimating parameters by maximizing the posterior distribution.
27
What is a conjugate prior?
A prior distribution that results in a posterior distribution of the same family.
28
What is the Markov Chain Monte Carlo (MCMC) method?
A computational technique used to approximate complex probability distributions.
29
What is the difference between Frequentist and Bayesian statistics?
Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.
30
What is Bayesian regression?
A form of regression that incorporates prior distributions on parameters.
31
What is Bayesian inference?
A method of updating beliefs based on prior knowledge and new evidence.
32
What is Bayes' Theorem?
P(A|B) = [P(B|A) * P(A)] / P(B).
33
What is a prior probability?
An initial probability before observing new data.
34
What is a likelihood function?
The probability of observed data given a parameter value.
35
What is a posterior probability?
An updated probability after considering new evidence.
36
What is Maximum A Posteriori (MAP) estimation?
A Bayesian method of estimating parameters by maximizing the posterior distribution.
37
What is a conjugate prior?
A prior distribution that results in a posterior distribution of the same family.
38
What is the Markov Chain Monte Carlo (MCMC) method?
A computational technique used to approximate complex probability distributions.
39
What is the difference between Frequentist and Bayesian statistics?
Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.
40
What is Bayesian regression?
A form of regression that incorporates prior distributions on parameters.
41
What is Bayesian inference?
A method of updating beliefs based on prior knowledge and new evidence.
42
What is Bayes' Theorem?
P(A|B) = [P(B|A) * P(A)] / P(B).
43
What is a prior probability?
An initial probability before observing new data.
44
What is a likelihood function?
The probability of observed data given a parameter value.
45
What is a posterior probability?
An updated probability after considering new evidence.
46
What is Maximum A Posteriori (MAP) estimation?
A Bayesian method of estimating parameters by maximizing the posterior distribution.
47
What is a conjugate prior?
A prior distribution that results in a posterior distribution of the same family.
48
What is the Markov Chain Monte Carlo (MCMC) method?
A computational technique used to approximate complex probability distributions.
49
What is the difference between Frequentist and Bayesian statistics?
Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.
50
What is Bayesian regression?
A form of regression that incorporates prior distributions on parameters.
51
What is Bayesian inference?
A method of updating beliefs based on prior knowledge and new evidence.
52
What is Bayes' Theorem?
P(A|B) = [P(B|A) * P(A)] / P(B).
53
What is a prior probability?
An initial probability before observing new data.
54
What is a likelihood function?
The probability of observed data given a parameter value.
55
What is a posterior probability?
An updated probability after considering new evidence.
56
What is Maximum A Posteriori (MAP) estimation?
A Bayesian method of estimating parameters by maximizing the posterior distribution.
57
What is a conjugate prior?
A prior distribution that results in a posterior distribution of the same family.
58
What is the Markov Chain Monte Carlo (MCMC) method?
A computational technique used to approximate complex probability distributions.
59
What is the difference between Frequentist and Bayesian statistics?
Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.
60
What is Bayesian regression?
A form of regression that incorporates prior distributions on parameters.
61
What is Bayesian inference?
A method of updating beliefs based on prior knowledge and new evidence.
62
What is Bayes' Theorem?
P(A|B) = [P(B|A) * P(A)] / P(B).
63
What is a prior probability?
An initial probability before observing new data.
64
What is a likelihood function?
The probability of observed data given a parameter value.
65
What is a posterior probability?
An updated probability after considering new evidence.
66
What is Maximum A Posteriori (MAP) estimation?
A Bayesian method of estimating parameters by maximizing the posterior distribution.
67
What is a conjugate prior?
A prior distribution that results in a posterior distribution of the same family.
68
What is the Markov Chain Monte Carlo (MCMC) method?
A computational technique used to approximate complex probability distributions.
69
What is the difference between Frequentist and Bayesian statistics?
Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.
70
What is Bayesian regression?
A form of regression that incorporates prior distributions on parameters.
71
What is Bayesian inference?
A method of updating beliefs based on prior knowledge and new evidence.
72
What is Bayes' Theorem?
P(A|B) = [P(B|A) * P(A)] / P(B).
73
What is a prior probability?
An initial probability before observing new data.
74
What is a likelihood function?
The probability of observed data given a parameter value.
75
What is a posterior probability?
An updated probability after considering new evidence.
76
What is Maximum A Posteriori (MAP) estimation?
A Bayesian method of estimating parameters by maximizing the posterior distribution.
77
What is a conjugate prior?
A prior distribution that results in a posterior distribution of the same family.
78
What is the Markov Chain Monte Carlo (MCMC) method?
A computational technique used to approximate complex probability distributions.
79
What is the difference between Frequentist and Bayesian statistics?
Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.
80
What is Bayesian regression?
A form of regression that incorporates prior distributions on parameters.
81
What is Bayesian inference?
A method of updating beliefs based on prior knowledge and new evidence.
82
What is Bayes' Theorem?
P(A|B) = [P(B|A) * P(A)] / P(B).
83
What is a prior probability?
An initial probability before observing new data.
84
What is a likelihood function?
The probability of observed data given a parameter value.
85
What is a posterior probability?
An updated probability after considering new evidence.
86
What is Maximum A Posteriori (MAP) estimation?
A Bayesian method of estimating parameters by maximizing the posterior distribution.
87
What is a conjugate prior?
A prior distribution that results in a posterior distribution of the same family.
88
What is the Markov Chain Monte Carlo (MCMC) method?
A computational technique used to approximate complex probability distributions.
89
What is the difference between Frequentist and Bayesian statistics?
Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.
90
What is Bayesian regression?
A form of regression that incorporates prior distributions on parameters.
91
What is Bayesian inference?
A method of updating beliefs based on prior knowledge and new evidence.
92
What is Bayes' Theorem?
P(A|B) = [P(B|A) * P(A)] / P(B).
93
What is a prior probability?
An initial probability before observing new data.
94
What is a likelihood function?
The probability of observed data given a parameter value.
95
What is a posterior probability?
An updated probability after considering new evidence.
96
What is Maximum A Posteriori (MAP) estimation?
A Bayesian method of estimating parameters by maximizing the posterior distribution.
97
What is a conjugate prior?
A prior distribution that results in a posterior distribution of the same family.
98
What is the Markov Chain Monte Carlo (MCMC) method?
A computational technique used to approximate complex probability distributions.
99
What is the difference between Frequentist and Bayesian statistics?
Frequentists rely on long-run frequencies, while Bayesians update beliefs using probabilities.
100
What is Bayesian regression?
A form of regression that incorporates prior distributions on parameters.

Statistics for Machine Learning (9 decks)

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