EmptyParameter

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

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3. final def ==(arg0: Any): Boolean

   

   Definition Classes

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

   

   Adds the value delta to the parameters

   Adds the value delta to the parameters

   Definition Classes

   Parameters

5. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

6. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

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   Annotations

   @throws( ... )

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

   

   Definition Classes

   AnyRef

8. def equals(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

9. def finalize(): Unit

   

   Attributes

   protected[java.lang]

   Definition Classes

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   Annotations

   @throws( classOf[java.lang.Throwable] )

10. def flatten: Vector[Nothing]

    

    Definition Classes

    EmptyParameter → Parameters

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

    

    Definition Classes

    AnyRef → Any

12. def getNode(i: Int): LeafParameter

    

    Get the leaf parameter at the ith node from the left

    Get the leaf parameter at the ith node from the left

    Definition Classes

    Parameters

13. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

14. def length: Int

    

    Definition Classes

    Parameters

15. def map(f: (Double) ⇒ Double): Parameters

    

    Definition Classes

    EmptyParameter → Parameters

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

    

    Definition Classes

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17. final def notify(): Unit

    

    Definition Classes

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18. final def notifyAll(): Unit

    

    Definition Classes

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19. def perturb(delta: Double): Rand[Parameters]

    

    Definition Classes

    EmptyParameter → Parameters

20. 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

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

    returns

    a distribution over the parameters which can be drawn from

    Definition Classes

    Parameters

21. 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

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

    eigen

    the eigenvalue decomposition of the covariance matrix of the proposal distribution

    returns

    a distribution over the parameters which can be drawn from

    Definition Classes

    Parameters

22. def size: Int

    

    Definition Classes

    Parameters

23. def sum(that: Parameters): Try[Parameters]

    

    Definition Classes

    EmptyParameter → Parameters

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

    

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    AnyRef

25. def toMap: Map[String, Double]

    

    Definition Classes

    Parameters

26. final def wait(): Unit

    

    Definition Classes

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    Annotations

    @throws( ... )

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

    

    Definition Classes

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    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )