Following Han et al, (2012), we adapt the well-known Gibbs sampling algorithm (Griffiths and Steyvers, 2004) to our model.
we compute the joint posterior distribution of hidden variables, denoted by P (z,ẽ,ẽ |D), and then use this distribution to 1) estimate θ, φ, ψ and ξ, and 2) predict translations and topics of all documents in D. we derive the joint posterior distribution from Eq.