• All Implemented Interfaces:
    RandomNumberGenerator, Seedable
    Direct Known Subclasses:
    HiddenMarkovModel, KnightSatchellTran1995

    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:
    Wikipedia: Hidden Markov model
    • Constructor Detail

      • HMMRNG

        public HMMRNG​(Vector PI,
                      Matrix A,
                      RandomNumberGenerator[] B)
        Constructs 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

        public HMMRNG​(HMMRNG that)
        Copy constructor.
        that - a HiddenMarkovModel
    • Method Detail

      • seed

        public void seed​(long... seeds)
        Description copied from interface: Seedable
        Seed the random number/vector/scenario generator to produce repeatable experiments.
        Specified by:
        seed in interface Seedable
        seed in class SimpleMC
        seeds - the seeds
      • next

        public HmmInnovation next()
        Gets the next simulated innovation: state and observation.
        the next HMM innovation