 Hidden Markov models are generative models, in which the joint distribution of observations and hidden states, or equivalently both the prior distribution of hidden states (the "transition probabilities") and conditional distribution of observations given states (the "emission probabilities"), is modeled.The above algorithms implicitly assume a uniform prior distribution over the transition probabilities.However, it is also possible to create hidden Markov models with other types of prior distributions.