Module 11: Probabilistic Reasoning and Bayes' Nets

Question 1

T/F
The conditional probability distribution of a variable in a Bayesian network should be specified based on the probability distributions of all of the other variables (nodes).

Answer 1

False

Question 2

T/F
Every variable in a Bayes net is independent of all of its descendants given its children.

Answer 2

False

Question 3

Consider the Bayes Net image by following this link:

https://artificial-intelligence-class.org/assets/img/quiz9bayes.pngLinks to an external site.

Write the joint probability for the Bayes’ Net given above, encoding its independence assumptions into your equation.

Answer 3

𝑃(𝐴)∗𝑃(𝐵)∗𝑃(𝐶|𝐴)∗𝑃(𝐸|𝐵,𝐶)∗𝑃(𝐷|𝐴,𝐶)

Definition of Bayes Net.

Question 4

Which of the following is true of locally structured (sparse) systems? Select all that apply.

Answer 4

The structure grows linearly in complexity (rather than exponentially).

Question 5

T/F
More than one Bayesian network can be used to represent the same joint distribution.

Answer 5

True

Question 6

If two variables (nodes) X and Y in a Bayesian network do not share a path, which of the following must be true?

Answer 6

X and Y are conditionally independent.