I was reading Bayes Point Machine example from infer.net: http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Bayes%20Point%20Machine%20tutorial.aspx
The problem is when we have some input features that we want to classify. The classification goal is to learn a weight vector. Output is classified using this condition:
W x attributes >= 0 (then class = true or 1, otherwise it's false or zero)
My question is: why for the weight vector we should use a VectorGaussian which is a multivariate normal distribution, and why the elements of weight matrix can't be just modeled using independent normal priors?
I implemented it using independent normal priors with mean zero and variance 1, and there is a dramatic change in the results.