**John Guiver replied on 04-23-2010 12:27 PM**

I'm not sure exactly what you're asking here. The Bayes Point Machine example defines a model which has a unknown parameter vector w which has a vector Gaussian prior. We try to explain the observed data by calculating the inner product with
an input vector and assigning one class ('true') if it is greater than 0, and the other class ('false') if it is less than zero. If we are in training mode, we observe the 'willBuy' value for each datum, and the inference then calculates
a posterior VectorGaussian distribution for the parameter vector w. In run mode, this distribution now acts as a prior, and we infer the willBuy value.

Also, see
http://community.research.microsoft.com/forums/t/4370.aspx for a slight extension of this example which adds noise before the IsPositive factor.

John G