## bayesian networks đź”—

### markov blanket đź”—

Source: Barberâ€™s BRML:

### d-separation đź”—

#### tl;dr đź”—

#### Active paths (source) đź”—

Simple case: empty conditioning set

Consider

- A -> C -> B
- A <- C <- B
- A <- C -> B
- A -> C <- B

If interpreted causally,

- A indirectly causes B
- B indirectly causes A
- C is a common cause of A and B

All of the first three causal situations give rise to association, or

**dependence**, between A and B => active paths. However, in case 4, A and B have a common effect in C but**no causal connection between them**; C is called a collider. Intuitively, non-colliders*transmit information (dependence)*while non-colliders donâ€™t. When the conditioning set is empty, d-separation is a simple matter of whether there are any paths between X and Y with no colliders (active).C in conditioning set

Now Câ€™s active-ness flip-flops. Conditioning on C makes A and B independent for the first 3 cases; C is now inactive.

- and 2.: Conditioning on C blocks the path from A to B.

3: Conditioning on a common cause C makes its effects, A and B, independent of each other. For the 4th case, C is now active when itâ€™s in the conditioning set. E.g.

`dead battery -> car wonâ€™t start <- no gas`

Knowing about the battery says nothing about the gas, but FIRST knowing that the car wonâ€™t start and then knowing about the battery tells you that thereâ€™s no gas. So, independent causes are made dependent by conditioning on a common effect (car wonâ€™t start). Finally, if conditioning on a collider activates the path, so does conditioning on any of its descendants.

## the multivariate Gaussian distribution đź”—

### comparing gaussian quadratic form to obtain mean and covariance, instead of completing the square đź”—

Source: Bishopâ€™s PRML chapter 2.3