A Bayesian Boosting Model
  We offer a novel view of AdaBoost in a statistical setting. We propose a Bayesian model for binary classification in which label noise is modeled hierarchically. Using variational inference to optimize a dynamic evidence lower bound, we derive a new boosting-like algorithm called VIBoost. We show its close connections to AdaBoost and give experimental results from four datasets. 
Alexander Lorbert and David M. Blei and Robert E. Schapire and Peter   J. Ramadge
stat.ML