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Which of the following are true about the junction tree algorithm?

Which of the following are true about the junction tree algorithm?

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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Which of the following are true about the junction tree algorithm? The subgraph of the junction tree nodes containing a Bayesian network (or Markov random field) node can be disconnected. We make variational inference with the best approximate distribution in terms of maximizing its K-L divergence with the original intractable distribution. The joint posterior probability of a hidden Markov model can be written in terms of the marginal posterior probabilities: $$p(x_1.x_T]y_1,..y_T)=\prod_t p(x_t,x_{t_1}[y_1,...y_T)/\prod_t p(x_t\y_1,...y_T)$$. Running intersection property is automatically satisfied if we construct maximum spanning tree connecting maximum cliques of triangulated Markov random field. Each factor of the original Bayesian network (or Markov random field) should appear in one and only one junction tree node. a Bayesian network (or Markov random field) node can appear in multiple junction tree nodes. The time complexity of inferring all probability distributions about the junction tree nodes is square of the number of junction tree nodes, because finding the probability distribution about each junction tree node involves message passing from all nodes.
Transcribed Image Text:Which of the following are true about the junction tree algorithm? The subgraph of the junction tree nodes containing a Bayesian network (or Markov random field) node can be disconnected. We make variational inference with the best approximate distribution in terms of maximizing its K-L divergence with the original intractable distribution. The joint posterior probability of a hidden Markov model can be written in terms of the marginal posterior probabilities: $$p(x_1.x_T]y_1,..y_T)=\prod_t p(x_t,x_{t_1}[y_1,...y_T)/\prod_t p(x_t\y_1,...y_T)$$. Running intersection property is automatically satisfied if we construct maximum spanning tree connecting maximum cliques of triangulated Markov random field. Each factor of the original Bayesian network (or Markov random field) should appear in one and only one junction tree node. a Bayesian network (or Markov random field) node can appear in multiple junction tree nodes. The time complexity of inferring all probability distributions about the junction tree nodes is square of the number of junction tree nodes, because finding the probability distribution about each junction tree node involves message passing from all nodes.
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