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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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12.  def hashCode(): Int

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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

    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)): Rand[(A, Double)]

    A Single Step without acceptance ratio

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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25.  def toString(): String

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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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