scalation.state

MarkovC

class MarkovC extends Error

This class supports the creation and use of Continuous-Time Markov Chains (CTMC). Note: the transition matrix tr gives the state transition rates off-diagonal. The diagonal elements must equal minus the sum of the rest of their row. Transient solution: Solve the Chapman-Kolmogorov differemtial equations. Equilibrium solution (steady-state): solve for p in p * tr = 0. See: www.math.wustl.edu/~feres/Math450Lect05.pdf

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Instance Constructors

  1. new MarkovC(tr: MatrixD)

    tr

    the transition rate matrix

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. def animate(): Unit

    Animate this continuous-time Markov Chain.

    Animate this continuous-time Markov Chain. Place the nodes around a circle and connect them if there is a such a transition.

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

    Attributes
    protected[lang]
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    @throws()
  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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    protected[lang]
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  12. def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

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  13. final def getClass(): java.lang.Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. val jump: MatrixD

    The jump matrix derived from the transition rate matrix (tr)

  17. def limit: VectorD

    Compute the limiting probabilistic state as t -> infinity, by finding the left nullspace of the tr matrix: solve for p such that p * tr = 0 and normalize p, i.

    Compute the limiting probabilistic state as t -> infinity, by finding the left nullspace of the tr matrix: solve for p such that p * tr = 0 and normalize p, i.e., ||p|| = 1.

  18. final def ne(arg0: AnyRef): Boolean

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  19. def next(p: VectorD, t: Double = 1.): VectorD

    Compute the next probabilistic state at t time units in the future.

    Compute the next probabilistic state at t time units in the future.

    p

    the current state probability vector

    t

    compute for time t

  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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  22. def simulate(i0: Int, endTime: Double): Unit

    Simulate the continuous-time Markov chain, by starting in state i0 and after the state's holding, making a transition to the next state according to the jump matrix.

    Simulate the continuous-time Markov chain, by starting in state i0 and after the state's holding, making a transition to the next state according to the jump matrix.

    i0

    the initial/start state

    endTime

    the end time for the simulation

  23. final def synchronized[T0](arg0: ⇒ T0): T0

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  24. def toString(): String

    Convert this continuous-time Markov Chain to s string.

    Convert this continuous-time Markov Chain to s string.

    Definition Classes
    MarkovC → AnyRef → Any
  25. final def wait(): Unit

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  26. final def wait(arg0: Long, arg1: Int): Unit

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  27. final def wait(arg0: Long): Unit

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Inherited from Error

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