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