- This  content of roadmap follows Prof. Jordan's textbook and includes additional material we discuss in class.
+ This  content of roadmap follows Prof. Jordan's lectures/textbook.
  
  
  ## Conditional Independence and Factorization
  
- *   Much of our early discussion focused on[[conditional independence]] in the context of [directed graphical models](Bayesian networks) and [undirected graphical models](Markov random fields)
+ *   Much of our early discussion focused on [[conditional independence]] in the context of [directed graphical models (Bayes nets)](Bayesian networks) and [undirected graphical models (Markov random fields)](Markov random fields)
- *  W
+ * We can use the [[Bayes Ball]] algorithm to determine conditional independencies in  Bayes nets.