Parameters

abstract def flatten: Vector[Double]

abstract def map(f: (Double) ⇒ Double): Parameters

abstract def perturb(delta: Double): Rand[Parameters]

abstract def sum(that: Parameters): Try[Parameters]

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def add(delta: DenseVector[Double]): Parameters

Adds the value delta to the parameters

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getNode(i: Int): LeafParameter

Get the leaf parameter at the ith node from the left

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def length: Int

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def perturbMvn(chol: DenseMatrix[Double])(implicit rand: RandBasis = Rand): Rand[Parameters]

Propose a new value of the parameters using a Multivariate Normal distribution Using the cholesky decomposition of the covariance matrix

def perturbMvnEigen(eigen: EigSym[DenseVector[Double], DenseMatrix[Double]], scale: Double)(implicit rand: RandBasis = Rand): Rand[Parameters]

Propose a new value of the parameters using a Multivariate Normal distribution using the eigenvalue decomposition of the covariance matrix

def size: Int

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

def toMap: Map[String, Double]

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit