Package dev.nm.stat.markovchain

Class SimpleMC

java.lang.Object
dev.nm.stat.markovchain.SimpleMC
All Implemented Interfaces:
RandomNumberGenerator, Seedable
Direct Known Subclasses:
HMMRNG
public class SimpleMC extends Object implements RandomNumberGenerator
This is a time-homogeneous Markov chain with a finite state space. It is a mathematical system that undergoes transitions from one state to another, between a finite or countable number of possible states. It is a random process characterized as memoryless: the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of "memorylessness" is called the Markov property.
See Also:

  • Constructor Details

    • SimpleMC

      public SimpleMC(Vector PI, Matrix A)
      Constructs a time-homogeneous Markov chain with a finite state space.
      Parameters:
      PI - the initial state probabilities
      A - the state transition probabilities

  • Method Details

    • 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
      Parameters:
      seeds - the seeds

    • nextDouble

      public double nextDouble()
      Gets the next simulated state.
      Specified by:
      nextDouble in interface RandomNumberGenerator
      Returns:
      next state

    • nextState

      public int nextState()
      Gets the next simulated state.
      Returns:
      next state

    • PI

      public ImmutableVector PI()
      Gets the initial state probabilities.
      Returns:
      the initial state probabilities

    • A

      public ImmutableMatrix A()
      Gets the state transition probabilities.
      Returns:
      the state transition probabilities

    • nStates

      public int nStates()
      Gets the number of states.
      Returns:
      the number of states

    • getStationaryProbabilities

      public static Vector getStationaryProbabilities(Matrix A)
      Gets the stationary state probabilities of a Markov chain that is irreducible, aperiodic and strongly connected (positive recurrent). A stationary distribution vector x (if exists) satisfies \[ x' \times A = x' \] where x' denotes the transpose of x, and the sum of all elements of x is 1.
      Parameters:
      A - the transition matrix
      Returns:
      the stationary state probabilities

    • bin

      public static int bin(MultinomialRVG rvg)
      Picks the first non-empty bin.
      Parameters:
      rvg - a MultinomialRVG
      Returns:
      the first non-empty bin