scalation.stat

BatchVector

class BatchVector extends AnyRef

The BatchVector class contains a StatVector for maintaining experimental data in multiple batches and methods for computing statistics on these data. Ex: It can be used to support the Method of Batch Means (MBM).

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Instance Constructors

  1. new BatchVector(name: String, _bSize: Int = 10, _nBatches: Int = 10)

    name

    name of the batch statistic

    _bSize

    size of each batch

    _nBatches

    number of batches

Value Members

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

    Definition Classes
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  2. final def ##(): Int

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

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  4. def acorr: Double

    Compute the lag 1 autocorrelation of the batch means.

    Compute the lag 1 autocorrelation of the batch means. Be sure to run 'computeMeans' first.

  5. def allocBatches(more: Int = 1): Unit

    Allocate additional batches for this batched vector.

    Allocate additional batches for this batched vector.

    more

    the number of additional batches to allocate

  6. final def asInstanceOf[T0]: T0

    Definition Classes
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  7. def bSize: Int

    Get the batch size.

  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )

  9. def computeMeans(): Unit

    Compute means for each batch and store them in the 'yb' stat vector.

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

    Definition Classes
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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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    protected[java.lang]
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  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
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  16. def len: Int

    Get the total length of the batched vector.

  17. def nBatches: Int

    Get the number of batches.

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

    Definition Classes
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  19. final def notify(): Unit

    Definition Classes
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  20. final def notifyAll(): Unit

    Definition Classes
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  21. def precise(threshold: Double = .2, p: Double = .95): Boolean

    Determine whether the Confidence Interval (CI) on the mean is tight enough.

  22. def precision(p: Double = .95): Double

    Compute the relative precision, i.

    Compute the relative precision, i.e., the ratio of the confidence interval half-width and the mean.

    p

    the confidence level

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

    Definition Classes
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  24. def tally(value: Double): Unit

    Add the given value in the next index position in the batched vector.

    Add the given value in the next index position in the batched vector.

    value

    the given value to add

  25. def toString(): String

    Convert the batched vector into a string showing each of the batches.

    Convert the batched vector into a string showing each of the batches.

    Definition Classes
    BatchVector → AnyRef → Any

  26. def uncorrelated(threshold: Double = .2): Boolean

    Determine whether the batches are sufficiently uncorrelated.

    Determine whether the batches are sufficiently uncorrelated.

    threshold

    the cut-off value to be considered uncorrelated

  27. final def wait(): Unit

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    @throws( ... )

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

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

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