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 (Bayes nets)](Bayesian networks) and [undirected graphical models (Markov random fields - MRFs)](Markov random fields)
  * We can use the [[Bayes Ball]] algorithm to determine conditional independencies in  Bayes nets.
  * We can use simple [reachability algorithms](http://en.wikipedia.org/wiki/Reachability) to determine conditional independencies in MRFs
  *  We briefly discussed [[factor graphs]], which provide a more fine-grained representation of the independencies in a MRF
+ 
+ ## Exact Inference
+ 
+ * variable elimination
+ 
+ junction trees
+ 
+ ## Approximate inference
+ 
+ * MCMC
+ * metropolis hastings