Metropolis

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

   

   Use particle marginal metropolis algorithm

7. def dlm(mod: Model, observations: Vector[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

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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 and keeping track of the acceptance ratio

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

    

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16. final def notify(): 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

    returns

    a distribution over the diagonal matrices

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 Double

    returns

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

20. def proposeVector(delta: Double)(a: DenseVector[Double]): Rand[DenseVector[Double]]

    

    Add a Random innovation to a DenseVector[Double] using the Gaussian distribution

    Add a Random innovation to a DenseVector[Double] using the Gaussian distribution

    delta

    the standard deviation of the innovation distribution

    a

    the starting value of the parameter

    returns

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

21. 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

22. def step[A](proposal: (A) ⇒ Rand[A], prior: (A) ⇒ Double, likelihood: (A) ⇒ Double)(state: (A, Double)): (A) ⇒ Rand[A]

    

    A Single Step without acceptance ratio this requires re-evaluating the likelihood at each step

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

    

    Propose a new value of the parameters on the log scale

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

    

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26. final def wait(): Unit

    

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

    

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

    

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