**Part Of**: [Advanced Inference In Graphical Models] sequence

**Followup To:** [Murderous Cliques]

Table Of Contents

- Context
- Forests & Trees
- Perfect Elimination
- Murderous Messages
- Takeaways

Context

Our takeaways from last time:

- Cliques are graph subsets that are fully connected.
- Variable elimination is a computational process that first bundles like factors together, and then eliminates a variable via “summing it out”.
- The order in which one eliminates irrelevant variables is computationally significant.
- Sometimes, variable elimination creates undesirable fill-in edges – edges that breach the family contract & expands the boundaries of the graphical model.
- Fill-in edges happen because the former neighbors of a fill-in edge must form a clique.
- In conclusion, when selecting the order in which to eliminate variables, if possible, choose the variable whose neighbors
*already*form a clique.

We can make our conclusion more specific.

Let **perfect elimination ordering** refer to exact inference algorithms that do not at any step introduce fill-in edges; F = {}.

In order to accomplish such an ordering, we should eliminate nodes that match either one of two **elimination principles**:

- have only zero or one neighbor
- have neighbors that are already fully connected.

In either case, fill-in edges will not be added because the clique requirement is already satisfied.

Forests & Trees

So far in this sequence, we have considered a wide variety of different graph types. Today, let us consider a special type of graph: the forest, and the tree.

Informally, a **forest** is a graph that has no cycles. A **tree** is a connected forest, a forest in which two nodes are connected by precisely one path.

Below is one forest comprising four trees:

Theorem 2 (Trees, Berge). Let T = (V, E) be an undirected graph with |V| = n > 2. Then each of the following properties are equivalent and each can be used to define a tree.

- G is connected, and has no cycles.
- G has n – 1 edges and has no cycles.
- G is connected and contains exactly n-1 edges.
- G has no cycles, and if an edge is added to G, exactly one cycle is created.
- G is connected, and if any edge is removed, the remaining graph is not connected.
- Every pair of vertices of G is connected by one unique path.
- G can be generated via the following process: Start with a node v. Repeatedly choose a next vertex, and connect it with an edge to exactly one previously chosen vertex.

Perfect Elimination

Do all graphs support a perfect elimination ordering (PEO)? Consider the following:

In the left image, a PEO does not exist: it doesn’t matter which node you chose, its two neighbors are not already-connected. The right image, in contrast, *does* contain a PEO (as was shown last time).

Notice how the left 4-cycle is not a tree, whereas the right chain is. Might other types of trees (besides chains) also support perfect elimination orderings? The answer turns out to be yes:

- The maximum clique size of a tree is 2 (cliques of size three would form a cycle, and trees do not contain cycles).
- Every tree with size > 1 has 2
**leaf nodes**(nodes in a tree with only one neighbor). - Variable elimination on trees do not destroy their tree-like nature.
- If you always choose to eliminate an edge node first, the neighboring clique is of size 1, which guarantees F = {}. Let us call this procedure
**leaf-first ordering**. - Locating leaf nodes on a tree is computationally straightforward.

Therefore, any leaf-first ordering on a tree will yield a perfect elimination order, and operate in O(N*r^2) time.

Two important facts to remember:

- The leaf-first ordering procedure does
*not*specify one particular ordering as correct. Rather, it denotes a group of orderings as equivalent and correct. - As a data structure, trees were chosen because they guarantee elimination principle #1.

Murderous Messages

Time to view leaf-first ordering in action. Consider a tree T with |V| = 15. Suppose that, after building this tree with statistical data, a scientist submits a query as to the relationship between two variables.

We know how to perform this inference: eliminate all variables not involved in the query itself. To perform such elimination perfectly, we perform leaf-first ordering:

In the right image, we “rooted” T on the query variables, and eliminated the irrelevant nodes starting from the “lowest” ones.

Pay attention to the way information flows with the above ordering. It is as if the query variables act as a collecting magnet, sucking information from the rest of the graph towards its nodes. On a Bayesian interpretation of this algorithm, we can meaningfully state that this leaf-first ordering is a collection of evidence. Let us call such transmission of evidence **message passing**.

We can then re-interpret leaf-first ordering in the following protocol:

Message Passing Protocol(MPP): A message may be sent from node i to a neighbor node j only after node i has received a message from all of its other neighbors.

We motivated the MPP based on trees, and elimination principle #1. But we will later MPP in the context of **chordal graphs**, which guarantee principle #2.

Two things to bear in mind as we close:

- The phrase
**murderous messages**is simply a playful concatenation of the more cantankerous “variable elimination message passing”. - Notice how the requirement for maximal efficiency (in the form of perfect elimination ordering) is what motivated us to connect an inference algorithm with notions of epistemic fidelity.

Takeaways

- A forest is a graph that has no cycles. A tree is a connected forest.
- In trees, perfect elimination ordering is accomplished by leaf-first ordering.
- Leaf-first ordering can be reasonably interpreted as the collection of evidence (a.k.a message passing, or
**belief propagation**).