Metropolis

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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. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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6. def dglm(mod: Model, observations: Array[Data], proposal: (Parameters) ⇒ Rand[Parameters], prior: (Parameters) ⇒ Double, initP: Parameters, n: Int): Process[State[Parameters]]

   

7. def dlm(mod: Model, observations: Array[Data], proposal: (Parameters) ⇒ Rand[Parameters], prior: (Parameters) ⇒ Double, initP: Parameters): Process[State[Parameters]]

   

   Run the metropolis algorithm for a DLM, using the kalman filter to calculate the likelihood

8. final def eq(arg0: AnyRef): Boolean

   

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9. def equals(arg0: Any): Boolean

   

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10. def finalize(): Unit

    

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11. final def getClass(): Class[_]

    

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13. final def isInstanceOf[T0]: Boolean

    

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14. def mStep[A](proposal: (A) ⇒ Rand[A], prior: (A) ⇒ Double, likelihood: (A) ⇒ Double)(state: State[A]): Rand[State[A]]

    

    Metropolis kernel without re-evaluating the likelihood from the previous time step

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

    

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16. final def notify(): Unit

    

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17. final def notifyAll(): Unit

    

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18. def proposeDiagonalMatrix(delta: Double)(m: DenseMatrix[Double]): Rand[DenseMatrix[Double]]

    

    Update the diagonal values of a covariance matrix by adding a Gaussian perturbation and ensuring the resulting diagonal is symmetric

    Update the diagonal values of a covariance matrix by adding a Gaussian perturbation and ensuring the resulting diagonal is symmetric

    delta

    the standard deviation of the innovation distribution

    m

    a diagonal DenseMatrix[Double], representing a covariance matrix

19. def proposeDouble(delta: Double)(a: Double): Rand[Double]

    

    Add a Random innovation to a numeric value using the Gaussian distribution

    Add a Random innovation to a numeric value using the Gaussian distribution

    delta

    the standard deviation of the innovation distribution

    a

    the starting value of the parameter

    returns

    a Rand[Double] representing a perturbation of the double a which can be drawn from

20. def rmvn(chol: DenseMatrix[Double])(implicit rand: RandBasis = Rand): Rand[DenseVector[Double]]

    

    Simulate from a multivariate normal distribution given the cholesky decomposition of the covariance matrix

21. def symmetricProposal(delta: Double)(p: Parameters): Rand[Parameters]

    

    Propose a new value of the parameters

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

    

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24. final def wait(): Unit

    

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

    

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

    

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