About clustering with Hough transform, why is it use if the prediction of the model location has large error bounds? How does the Bayesian analysis help to remove the outliers?

It would be nice to get a little more intuition about the relationship between scale invariance and the extrema of DoG or Laplacian of Gaussian than what is given in the Lowe paper.