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]( 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
+ *  The [[variable elimination]] algorithm  is based on interchanging sums and products in the definitions of marginals or partition functions but can perform many redundant calculations.
+ *  [the sum product algorithm](sum_product_on_trees)  is a belief propagation algorithm based on dynamic programming. It has the advantage over naive variable elimination in that it reuses computations to compute marginals for all nodes in the graph
+ *  [[junction trees]] generalize the the sum product algorithm to arbitrary graphs by  grouping variables together into cliques such that the cliques form a tree.
- * variable elimination
+ ## Sampling-based inference
+ *  [[rejection sampling]]
+ *  [[importance sampling]]
- junction trees
- ## Approximate inference
- * MCMC
  * metropolis hastings