Compute the marginal distributions p(y). Notes: Marginal probabilities are the building blocks for more complex probability ideas like Bayes Theorem which is used throughout machine learnin O [13/30, 7/30, 11/30] O [10/30, 5/30, 10/30] O [13/30, 7/30, 10/30] O [13/30, 10/30, 7/30]
Compute the marginal distributions p(y). Notes: Marginal probabilities are the building blocks for more complex probability ideas like Bayes Theorem which is used throughout machine learnin O [13/30, 7/30, 11/30] O [10/30, 5/30, 10/30] O [13/30, 7/30, 10/30] O [13/30, 10/30, 7/30]
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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Question
![From the following bivariate distribution p(x, y) of two discrete random
variables X and Y,
5
Y1
Y2
Y3
p(x)
X1
5
4
1
X2
3
2
0
O [13/30, 7/30, 11/30]
O [10/30, 5/30, 10/30]
O [13/30, 7/30, 10/30]
O [13/30, 10/30, 7/30]
X3
4
1
5
X4 p(y)
1
0
4
30/30
Compute the marginal distributions p(y).
Notes: Marginal probabilities are the building blocks for more complex
probability ideas like Bayes Theorem which is used throughout machine learning.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2e96339b-d3d9-4039-844d-3fc091742cc5%2F93fe97bc-788a-4eb1-942f-880f43b2117d%2F1ahfxni_processed.png&w=3840&q=75)
Transcribed Image Text:From the following bivariate distribution p(x, y) of two discrete random
variables X and Y,
5
Y1
Y2
Y3
p(x)
X1
5
4
1
X2
3
2
0
O [13/30, 7/30, 11/30]
O [10/30, 5/30, 10/30]
O [13/30, 7/30, 10/30]
O [13/30, 10/30, 7/30]
X3
4
1
5
X4 p(y)
1
0
4
30/30
Compute the marginal distributions p(y).
Notes: Marginal probabilities are the building blocks for more complex
probability ideas like Bayes Theorem which is used throughout machine learning.
![From the following bivariate distribution p(x, y) of two discrete random
variables X and Y,
Y1
Y2
Y3
p(x)
X1
5
4
1
X2
3
2
0
O [4/13,2/13, 1/13, 0]
O [5/10, 4/10, 1/10]
O [5/13, 3/13, 4/13, 1/13]
O [3/5, 2/5, 0]
x3
4
1
5
X4
1
0
4
p(y)
30/30
Compute the conditional distributions p(x | Y = y₁).
Notes: Additionally, conditional probabilities are more building blocks we need
to understand complex probability ideas like Bayes Theorem which is used
throughout machine learning.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2e96339b-d3d9-4039-844d-3fc091742cc5%2F93fe97bc-788a-4eb1-942f-880f43b2117d%2F9mg27jk_processed.png&w=3840&q=75)
Transcribed Image Text:From the following bivariate distribution p(x, y) of two discrete random
variables X and Y,
Y1
Y2
Y3
p(x)
X1
5
4
1
X2
3
2
0
O [4/13,2/13, 1/13, 0]
O [5/10, 4/10, 1/10]
O [5/13, 3/13, 4/13, 1/13]
O [3/5, 2/5, 0]
x3
4
1
5
X4
1
0
4
p(y)
30/30
Compute the conditional distributions p(x | Y = y₁).
Notes: Additionally, conditional probabilities are more building blocks we need
to understand complex probability ideas like Bayes Theorem which is used
throughout machine learning.
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