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class MarkovC extends Error

The MarkovC class supports the creation and use of Continuous-Time Markov Chains 'CTMC's. 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 differential equations. Equilibrium solution (steady-state): solve for 'p' in 'p * tr = 0'.

See also

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: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. 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.

  5. final def asInstanceOf[T0]: T0
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  6. def clone(): AnyRef
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    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: Any): Boolean
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  9. final def flaw(method: String, message: String): Unit
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  10. final def getClass(): Class[_]
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  11. def hashCode(): Int
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  12. final def isInstanceOf[T0]: Boolean
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  13. val jump: MatrixD

    The jump matrix derived from the transition rate matrix 'tr'

  14. 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.e., '||p|| = 1'.

  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  16. def next(p: VectorD, t: Double = 1.0): 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

  17. final def notify(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  18. final def notifyAll(): Unit
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  19. 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

  20. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  21. def toString(): String

    Convert 'this' continuous-time Markov Chain to s string.

    Convert 'this' continuous-time Markov Chain to s string.

    Definition Classes
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  22. final def wait(arg0: Long, arg1: Int): Unit
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  23. final def wait(arg0: Long): Unit
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  24. final def wait(): Unit
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Deprecated Value Members

  1. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

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