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 N(-2,0.1)
C | B=unstable N(-1,0.3)

If we assume a clique of \left \{ B,C \right \} with the potentials P(B) and P(C|B) assigned to it. By the literature being followed, we need to initialise these potentials in a canonical form.

Therefore the initialisation of B is:

g_{B}(stable) = log(0.85) = -0.16252

h_{B}(stable) = 0

k_{B}(stable) = 0

g_{B}(stable) = log(0.15) = -1.89712

h_{B}(stable) = 0

k_{B}(stable) = 0

The initialisation of C is:

g_{C}(stable) = -\frac{(-2)^{2}}{2*0.1}-\left \{ log(2\pi *0.1) \right \}/2=-19.76765

h_{C}(stable) = -2/0.1=-20

k_{C}(stable) = 1/0.1=10

g_{C}(unstable) = -\frac{(-1)^{2}}{2*0.3}-\left \{ log(2\pi *0.3) \right \}/2=-1.98362

h_{C}(unstable) = -1/0.3=-3.33333

k_{C}(unstable) = 1/0.3=3.33333

We add the canonical forms together to produce the canonical form for B,C:

g_{B,C}(stable) = -19.9302

h_{B,C}(stable) = -20

k_{B,C}(stable) = 10

g_{B,C}(unstable) = -3.88074

h_{B,C}(unstable) = -3.33333

k_{B,C}(unstable) = 3.33333

When I use Hugin I get the result for C - \mu_{C}=-1.85, \sigma ^{2}=0.257049. 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?