# Bayesian Network¶

Note

Here I use $$\perp\!\!\!\perp$$ as an independence symbol.

A Bayesian network(BN) is a directed acyclic graph (DAG) in which nodes represent random variables, whose joint distribution is as follows,

$p(x_1, ..., x_D) = \prod_{i=1}^D p\big(x_i| pa(x_i)\big)$

where $$pa(x_i)$$ represents the parents of $$x_i$$. < An example of a bayesian network. Source: Aalto course CS-E4820: Advanced probabilistic methods >

BNs are used in ML, because they are

• a concise way to represent and communicate the structure and assumptions of a model.
• a compact representation of the joint distribution. => efficient!

## Independence in Bayesian networks¶

### Example 1¶ Independent? D connection
$$A \perp\!\!\!\perp B$$
$$A \perp\!\!\!\perp B | C$$ Separated
$$A \perp\!\!\!\perp B | E$$ Separated
$$D \not\!\perp\!\!\!\perp E | C$$ Connected

### Example 2¶ < Conditional Independence > < Marginal Independence >

Here’s my real life example about marginal independence. Say you won a $1M lottery(C). You can either buy a$1M house(A) or buy a \$1M Ferrari(B). If you’ve bought a Ferrari, you haven’t bought a house for sure.

## Collider¶

A cllider (v-structure, head-to-head meeting) has two incoming arrows along a chosen path. < Source: Aalto course CS-E4820: Advanced probabilistic methods >

## D-Connection & D-Separation¶

• A path between variables A and B is blocked by a set of variables $$\mathcal{C}$$, if
• there is a collider in the path such that neither the collider nor any of its descendasnts is in the conditioning set $$\mathcal{C}$$.
• there is a non-collider in the path that is in the conditioning set $$\mathcal{C}$$.
• Sets of variables $$\mathcal{A}$$ and $$\mathcal{B}$$ are d-separated by $$\mathcal{C}$$ if all paths between $$\mathcal{A}$$ and $$\mathcal{B}$$ are blocked by $$\mathcal{C}$$.
• d-separation implies $$A \perp\!\!\!\perp B | C$$
• $$\mathcal{X}$$ and $$\mathcal{Y}$$ are d-separated by $$\mathcal{Z}$$ in $$G$$ iff they are not d-connected by $$\mathcal{Z}$$ in $$G$$.

### Bottom line¶

• Non-collider in the conditioning set $$\Rightarrow$$ Blocked $$\Rightarrow$$ d-separated $$\Rightarrow$$ conditionally independent BUT unconditionally dependent
• Collider or its descendants in the conditioning set $$\Rightarrow$$ Not blocked $$\Rightarrow$$ d-connected $$\Rightarrow$$ conditionally dependent BUT unconditionally independent

### Examples¶ < b d-separates a from e. {b,d} d-connect a from e. > < c and e are (unconditionally) d-connected. b d-connects a and e >

## Markov equivalence¶

Two graphs are Markov equivalent if they

• entail(need) the same conditional independencies
• equivalently have the same d-separations < A markov equivalent example >

## Graph¶

• Parent: pa(D) = {A,C}
• Children: ch(D) = E
• Family: A node itself and its parents.
• fa(E) = {B,D,E,F}
• Markov blanket: A node itself, its parents, children and the parents of its children.
• MB(B) = {A,B,C,D,E,F}