 For example, even though a fully connected HMM could always be used if enough training data is available it is often useful to constrain the model by not allowing arbitrary state transitions.In the same way it can be beneficial to embed the HMM into a greater structure; which, theoretically, may not be able to solve any other problems than the basic HMM but can solve some problems more efficiently when it comes to the amount of training data required.In the hierarchical hidden Markov model (HHMM) each state is considered to be a self-contained probabilistic model.