One brown bag contains 2 red marbles, 2 blue marbles, and 4 green marbles. The other black bag contains 3 red marbles, 2 blue marbles, and 5 green marbles. You also have a biased coin that shows heads with probability 0.7 and tails with probability 0.3. If the coin shows heads, you pick a marble from the brown bag (Br) and from the black bag (Bl) otherwise. You flip the coin, pick a marble randomly, and see that it is green. What is the probability that the green marble was picked from the black bag? Use Bayes Theorem and the table below to compute p(BIG).
One brown bag contains 2 red marbles, 2 blue marbles, and 4 green marbles. The other black bag contains 3 red marbles, 2 blue marbles, and 5 green marbles. You also have a biased coin that shows heads with probability 0.7 and tails with probability 0.3. If the coin shows heads, you pick a marble from the brown bag (Br) and from the black bag (Bl) otherwise. You flip the coin, pick a marble randomly, and see that it is green. What is the probability that the green marble was picked from the black bag? Use Bayes Theorem and the table below to compute p(BIG).
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
Related questions
Question
pick multiple answers on the second one !
![There are three different colors of marbles, red, blue, and green in two bags.
One brown bag contains 2 red marbles, 2 blue marbles, and 4 green marbles.
The other black bag contains 3 red marbles, 2 blue marbles, and 5 green
marbles.
You also have a biased coin that shows heads with probability 0.7 and tails with
probability 0.3. If the coin shows heads, you pick a marble from the brown bag
(Br) and from the black bag (BI) otherwise.
You flip the coin, pick a marble randomly, and see that it is green. What is the
probability that the green marble was picked from the black bag? Use Bayes
Theorem and the table below to compute p(Bl|G).
Brown Bag (Br)
Black Bag (BI)
Total
Red (R)
2
3
Blue (B)
2
2
Green
(G)
4
5
Total
18/18
Notes: Later in the semester we will take a closer look at how we can apply the
idea of Bayes Theorem to perform classification!](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2e96339b-d3d9-4039-844d-3fc091742cc5%2F3020b5ef-8a40-430e-b35a-f99203d49fc1%2Fk7dmkon_processed.png&w=3840&q=75)
Transcribed Image Text:There are three different colors of marbles, red, blue, and green in two bags.
One brown bag contains 2 red marbles, 2 blue marbles, and 4 green marbles.
The other black bag contains 3 red marbles, 2 blue marbles, and 5 green
marbles.
You also have a biased coin that shows heads with probability 0.7 and tails with
probability 0.3. If the coin shows heads, you pick a marble from the brown bag
(Br) and from the black bag (BI) otherwise.
You flip the coin, pick a marble randomly, and see that it is green. What is the
probability that the green marble was picked from the black bag? Use Bayes
Theorem and the table below to compute p(Bl|G).
Brown Bag (Br)
Black Bag (BI)
Total
Red (R)
2
3
Blue (B)
2
2
Green
(G)
4
5
Total
18/18
Notes: Later in the semester we will take a closer look at how we can apply the
idea of Bayes Theorem to perform classification!
![The idea of a derivative is most closely related to which of the following
concepts?
Notes: The idea of a derivative is crucial to understanding what a gradient is
doing! As well see, gradients and gradient decent will be the key to machine
learning.
Slope
□ The minium of a function
The maximum of a function
Rate of change](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2e96339b-d3d9-4039-844d-3fc091742cc5%2F3020b5ef-8a40-430e-b35a-f99203d49fc1%2Fr8vpxda_processed.png&w=3840&q=75)
Transcribed Image Text:The idea of a derivative is most closely related to which of the following
concepts?
Notes: The idea of a derivative is crucial to understanding what a gradient is
doing! As well see, gradients and gradient decent will be the key to machine
learning.
Slope
□ The minium of a function
The maximum of a function
Rate of change
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