Because acquiring the posteriors analytically is intractable, inference is performed utilizing Markov chain Monte Carlo algorithms to obtain samples from the posterior distributions of interest (Johnson 2008;Johnson and Goldwater 2009;Johnson and Demuth 2010;Sirts and Goldwater 2013).
as sampling-based models are costly to train on large amounts of data, we adopt the parsing-based method proposed in Sirts and Goldwater (2013) to use the trained AG model inductively on test data.