Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
Matthew Beal, Zoubin Ghahramani, Carl Rasmussen
We show that it is possible to extend hidden Markov models to have a countably inﬁnite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the inﬁnitely many transition parameters, leaving only three hyperparameters which can be learned from data. These three hyperparameters deﬁne a hierarchical Dirichlet process capable of capturing a rich set of transition dynamics. The three hyperparameters control the time scale of the dynamics, the sparsity of the underlying state-transition matrix, and the expected num- ber of distinct hidden states in a ﬁnite sequence. In this framework it is also natural to allow the alphabet of emitted symbols to be inﬁnite— consider, for example, symbols being possible words appearing in En- glish text.