- People have found outside references (like nodes "Helpful article about Bayesian Networks" and "Reference for How Bayesian Networks work") that could help clarify concepts that were discussed in "The Book of Why"

- Pearl goes into great detail about the development and usage of Bayesian networks. The structure of nodes allows easier access to previously discussed topics and concepts that were incorporated into this concept. In addition, it can be helpful to have nodes that summarize larger concepts, like "Relation between Bayesian Networks and Causal Diagrams" and "From objectivity to subjectivity."

How Nodes About Bayesian Networks in 1Cademy Helped Learning from "The Book of Why"

- In the "Bayesian Networks" node one proposal helped improved the node by adding an image
- In "Bayes Theorem Overview," multiple people contributed various child nodes that relate to this topic
- For example, "Alternate Bayes' Theorem Equation" explores this topic in a different way
- Another individual helped explain one relevant example discussed in "The Book of Why" in the node "Inverse probability: break-cognitive asymmetry" which had a child node explained by another person, "Data could be reversed," which explains another example Pearl discusses in "The Book of Why"

Collaborative Learning in 1Cademy Nodes for Bayesian Networks

- "Author's reasons behind criticism of Bayesian Network"
- "Relation between Bayesian Networks and Causal Diagrams"
- "From objectivity to subjectivity"

Outside References
- "Helpful article about Bayesian Networks"
- "Reference for"How Bayesian Networks work""

University of Michigan - Ann Arbor

Learning Bayesian Networks from 1Cademy

Specific Nodes that were Helpful for Learning Bayesian Networks

- before discussing Bayesian networks, Pearl discusses the history and significance, using examples to further explain the concepts, of Bayes' rule and inverse probability from pages 95 - 108
- Pearl then connected this concept to Bayesian networks by discussing belief propagation from pages 108 - 112
- references past sections: "Although Bayes didn't know it, his rule for inverse probability represents the simplest Bayesian network. We have seen this network in several guises now: Tea -> Scones, Disease -> Test, or, more generally, Hypothesis -> Evidence" (Pearl, 2018, p. 112)
- explains the junctions present in both Bayesian networks and causal diagrams, using examples, from pages 113-116
- from pages 117 - 122, Pearl discusses conditional probability tables in relation to Bayesian networks and also mentions inverse-probability (previously discussed starting pages 97-99)
- from pages 122 - 133, Pearl discusses Bayesian networks in various contexts
- for the rest of the chapter (pages 128 - 133), Pearl compares Bayesian networks and causal diagrams

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