## Hybrid Network Inference

I'm trying to figure out how to calculate the result mean and variance for a simple hybrid bayesian network comprised of 2 variables (to keep things simple for now).

The nodes are B (discrete) and C (continuous); C being a child node of B.

The distribution of B is:
B=stable 0.85
B=unstable 0.15

The distribution of C is:
C | B=stable
C | B=unstable

If we assume a clique of with the potentials and assigned to it. By the literature being followed, we need to initialise these potentials in a canonical form.

Therefore the initialisation of is:

The initialisation of is:

We add the canonical forms together to produce the canonical form for :

When I use Hugin I get the result for - . Now,I can achieve those same values without performing the canonical transformation, but what I want to know is how do I get those result values from the canonical forms in the potentials?