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
+ * 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