I have a Bayesian model that tries to predict a binary variable that I am modelling as logistic regression. The training data have lot of wrong labels*. Therefore, I think I might need to introduce some sort of slack (similar to slack variable that SVM). How can I do this?
If I model logistic regression ignoring errors in training data, is logistic regression robust?
*The logistic regression training-data is sampled from another Bayesian model therefore there is large variance in my Gibbs sampling.