A part of my network consists of 2 continuous parent nodes "mu" and "sigma^2", with continuous child node A, with distribution A~N(mu,sigma^2). The mean of A "mu" is not known, but follows some distribution. Also the variance of A "sigma^2" is not known, but follows some distribution. Two questions:

1. How to set the prior distributions on "mu" and "sigma^2", if they are different from a Normal distribution. (In the table of these nodes I only have to specify the mean and sigma2. This means assumption of a normal distribution?) For example, I want to give "mu" and "sigma^2" a beta-pert-distribution.

2. How to fill in the table for A? Opening the table will me make fill in Mean, mu, sigma^2, Variance. Filling in 1 and 1 for mu resp. sigma^2, does this mean that the parent distributions only influence the distribution of A(Mean,Variance) by means of a "disturbance" (like in the modelling example Temperature.net from the course the quality of the thermometer does)? Thus does it only add mu to Mean and sigma^2 to Variance?

I hope someone can help me.

Joost