For this reason, we propose an approximate inference algorithmthe Gibbs sampling algorithm for the entity-topic model by extending the well-known Gibbs sampling algorithm for LDA (Griffiths & Steyvers, 2004).
in Gibbs sampling, we first construct the posterior distribution P (z; e; ajD) P (z; e; ajD) , then this posterior distribution is used to: 1) estimate μ μ, Á Á, Ã Ã and » »; and 2) predict the entities and the topics of all documents in D. we first derive the joint posterior distribution from Eq.