public class HMMRNG extends SimpleMC
In a (discrete) hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible. Each state has a probability distribution over the possible output tokens (could be continuous). Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states. Note that the adjective 'hidden' refers to the state sequence through which the model passes, not to the parameters of the model; even if the model parameters are known exactly, the model is still 'hidden'. In other words, a hidden Markov model is a Markov chain of (hidden) states and for each state a conditional random number generator (distribution).
- See Also:
HMMRNGConstructs a hidden Markov model.
PI- the initial state probabilities
A- the state transition probabilities of the homogeneous hidden Markov chain
B- the conditional observation random number generators (distributions)
(HMMRNG that)Copy constructor.
seedpublic void seed
(long... seeds)Description copied from interface:
SeedableSeed the random number/vector/scenario generator to produce repeatable experiments.
nextDoublepublic double nextDouble()Gets the next simulated observation.
nextpublic HmmInnovation next()Gets the next simulated innovation: state and observation.
- the next HMM innovation