Reviews: Streaming Bayesian Inference for Crowdsourced Classification

This paper proposes two algorithms for recovering ground truth labels in crowd sourcing tasks for binary classisification. The problem is formulated as an online Bayesian version of the Dawid & Skene model (with beta priors) which is quite natural. The algorithms are based on variational approximations of the posterior (i.e. they try to find the best approximation that is product distribution). From this approach two algorithms are derived. The other one is more accurate and but slower (still polynomial time).