Hello,

First I would like to say I read AIMA 2nd edition for fun and it was fantastic.

I have been studying the hybrid Bayesian network for an application and am
wearing out
pages 501 and 502 which talk about combining a discrete true/false parent
(Subsidy) and
a continuous parent (Harvest) into a linear Gaussian distribution.

I am simply not seeing where the variables required as parameters for the
Gaussian are
coming from. It seems that the h variable is the Harvest, and Subsidy would be
some
probability.

The extra variables are:
a-true, b-true, stddev-true
a-false, b-false, stddev-false

Would someone take pity on me? :)

Thanks,
Scott