# Posterior probabilities with decision trees or decision forests

Is there a way to get posterior probabilities $P(C | \vec{x})$ (probability that a data item $\vec{x}$ belong to one of the given classes) in a multiclass classification problem using decision trees or forests?

I found some hints about using calibration methods (e.g. Platt's method or Isotonic Regression) in combination with boosted or bagged trees. However, as I'm not experienced in this field I can't find a good explanation how this works. It would be very helpful if one of you could explain me the general idea how to get posterior probabilities with decision trees or a good link or paper where these things are explained.