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class Markov extends Error

The Markov class supports the creation and use of Discrete-Time Markov Chains 'DTMC's. Transient solution: compute the next state 'pp = p * tr' where 'p' is the current state probability vector and 'tr' is the transition probability matrix. Equilibrium solution (steady-state): solve for 'p' in 'p = p * tr'.

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Instance Constructors

  1. new Markov(tr: MatriD)

    tr

    the transition probability matrix

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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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 Markov Chain.

    Animate this 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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    protected[lang]
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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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    @native() @HotSpotIntrinsicCandidate()
  11. def hashCode(): Int
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    @native() @HotSpotIntrinsicCandidate()
  12. final def isInstanceOf[T0]: Boolean
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  13. def isStochastic: Boolean

    Check whether the transition matrix is stochastic.

  14. def limit: VectoD

    Compute the limiting probabilistic state 'p * tr^k' as 'k -> infinity', by solving a left eigenvalue problem: 'p = p * tr' => 'p * (tr - I) = 0', where the eigenvalue is 1. Solve for p by computing the left nullspace of the 'tr - I' matrix (appropriately sliced) and then normalize 'p' so '||p|| = 1'.

  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  16. def next(p: VectoD, k: Int = 1): VectoD

    Compute the 'k'th next probabilistic state 'p * tr^k'.

    Compute the 'k'th next probabilistic state 'p * tr^k'.

    p

    the current state probability vector

    k

    compute for the 'k'th step/epoch

  17. final def notify(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  18. final def notifyAll(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  19. def simulate(i0: Int, endTime: Int): Unit

    Simulate the discrete-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 discrete-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' discrete-time Markov Chain to a string.

    Convert 'this' discrete-time Markov Chain to a string.

    Definition Classes
    Markov → AnyRef → Any
  22. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  23. final def wait(arg0: Long): Unit
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    @throws( ... ) @native()
  24. final def wait(): Unit
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    @throws( ... )

Deprecated Value Members

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

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