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QUESTION 19 From the choices below, select the equation that shows how to compute an entry from the full joint probability distribution of the variables in a Bayesian network. P(x₁,...,xn) = {"=1 P(x₁ | parents(X;)) OP(x1₁,...,xn) =^=₁ P(xi | parents(X;)) OP(x1,...,xn) = {i=1 P(X₁| Parents(X;)) OP(x₁,...,xn) = "=1 P(X₁| Parents(X;))

QUESTION 19 From the choices below, select the equation that shows how to compute an entry from the full joint probability distribution of the variables in a Bayesian network. P(x₁,...,xn) = {"=1 P(x₁ | parents(X;)) OP(x1₁,...,xn) =^=₁ P(xi | parents(X;)) OP(x1,...,xn) = {i=1 P(X₁| Parents(X;)) OP(x₁,...,xn) = "=1 P(X₁| Parents(X;))

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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QUESTION 19 From the choices below, select the equation that shows how to compute an entry from the full joint probability distribution of the variables in a Bayesian network. O P(x1,...,xn) = {i=1 P(x₁ | parents(X;)) P(x₁,...,xn) = "=1 P(x₁ | parents(X;)) P(x1,...,xn) = {n-1 P(X₁| Parents(X;)) OP(x₁,...,xn) = ¹=1 P(X₁| Parents(X;)) QUESTION 20 From the choices below, select the equation that shows how to perform exact inference (answer queries) using the distribution encoded in a Bayesian network. P(X| e) = α Пy Σ₁-1 P(x;| parents(X₁) P(X | е) = α ¹у П²-₁ P(x¡ | parents(X;) P(X | e) = α y Пn-1 P(x;| parents(X;) P(X | e) = a Ey Σ" i=1 P(xi| parents(X₁) QUESTION 21 Is exact inference using this equation computationally tractable? Select the best answer. O No, because there are an exponential number of combinations of values for the hidden variables, and there are O(n) such variables in the worst case. O Yes, because we are summing over the hidden variables. O No, because there are n variables and there are an exponential number of combinations of values for those variables. O No, because there are an exponential number of terms from the network in the expression for the product, in the worst case.
Transcribed Image Text:QUESTION 19 From the choices below, select the equation that shows how to compute an entry from the full joint probability distribution of the variables in a Bayesian network. O P(x1,...,xn) = {i=1 P(x₁ | parents(X;)) P(x₁,...,xn) = "=1 P(x₁ | parents(X;)) P(x1,...,xn) = {n-1 P(X₁| Parents(X;)) OP(x₁,...,xn) = ¹=1 P(X₁| Parents(X;)) QUESTION 20 From the choices below, select the equation that shows how to perform exact inference (answer queries) using the distribution encoded in a Bayesian network. P(X| e) = α Пy Σ₁-1 P(x;| parents(X₁) P(X | е) = α ¹у П²-₁ P(x¡ | parents(X;) P(X | e) = α y Пn-1 P(x;| parents(X;) P(X | e) = a Ey Σ" i=1 P(xi| parents(X₁) QUESTION 21 Is exact inference using this equation computationally tractable? Select the best answer. O No, because there are an exponential number of combinations of values for the hidden variables, and there are O(n) such variables in the worst case. O Yes, because we are summing over the hidden variables. O No, because there are n variables and there are an exponential number of combinations of values for those variables. O No, because there are an exponential number of terms from the network in the expression for the product, in the worst case.
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