We use mean-field variational inference as an efficient approximation to full likelihood maximization by assuming a fully factorized approximate model: and then minimizing Kullback-Leibler divergence KL(q p) via coordinate ascent by iteratively updating the variational parameters ν.
q(θ | ν (δ) ) ∼ Dir(ν (δ) ) where where S(j) is the set of measurements provided by annotator j.