For the transition probability of hidden states, we put a HPY process prior as (Blunsom and Cohn, 2011): with the final base measure being a uniform distribution over the states.
to model this property, NPYLM assumes that word prior G 0 is generated from character HPYLM to model a well-formedness of w. In practice, to avoid dependency on n in the character model, we used an ∞-gram VPYLM (Mochihashi and Sumita, 2008) in this research.