Distribution

Value Members

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

   Definition Classes

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. def F(d1: Int, d2: Int): Distribution[Double]

7. object H extends Coin with Product with Serializable

8. object T extends Coin with Product with Serializable

9. def always[A](value: A): Distribution[A]

10. final def asInstanceOf[T0]: T0

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11. def bernoulli(p: Double = 0.5): Distribution[Int]

12. def beta(a: Double, b: Double): Distribution[Double]

13. def biasedCoin(p: Double): Distribution[Coin]

14. def binomial(p: Double, n: Int): Distribution[Int]

15. def cauchy: Distribution[Double]

16. def chi2(n: Int): Distribution[Double]

17. def chi2test[A, B](d: Distribution[(A, B)]): Double

    Determine if a joint probability distribution is composed of 2 independent events.

    Determine if a joint probability distribution is composed of 2 independent events. Uses the G-test: http://en.wikipedia.org/wiki/G-test

18. def choose(n: Int, k: Int): Distribution[List[Boolean]]

19. def clone(): AnyRef

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20. def coin: Distribution[Coin]

21. def d(n: Int): Distribution[Int]

22. def definitely[A](value: A): Distribution[A]

23. def dice(n: Int): Distribution[List[Int]]

24. def die: Distribution[Int]

25. def dirichlet(alphas: List[Double]): Distribution[List[Double]]

26. def discrete[A](weightedValues: (A, Double)*): Distribution[A]

27. def discreteUniform[A](values: Iterable[A]): Distribution[A]

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

    Definition Classes

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29. def equals(arg0: Any): Boolean

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30. def exponential(l: Double): Distribution[Double]

31. def finalize(): Unit

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    protected[lang]

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32. def gamma(k: Double, theta: Double): Distribution[Double]

33. def geometric(p: Double): Distribution[Int]

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

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35. def hashCode(): Int

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36. final def isInstanceOf[T0]: Boolean

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37. def ksTest[A](d1: Distribution[A], d2: Distribution[A])(implicit ord: Ordering[A]): Double

    Tests if two probability distributions are the same using the Kolmogorov-Smirnov test.

    Tests if two probability distributions are the same using the Kolmogorov-Smirnov test. The distributions are unlikely to be the same (p < 0.05) if the value is greater than 1.35 and very unlikely (p < 0.001) if the value is greater than 1.95.

38. def laplace(b: Double): Distribution[Double]

39. def lognormal: Distribution[Double]

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

    Definition Classes

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41. def negativeBinomial(p: Double, r: Int): Distribution[Int]

42. object normal extends Distribution[Double]

43. final def notify(): Unit

    Definition Classes

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44. final def notifyAll(): Unit

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45. def pareto(a: Double, xm: Double = 1.0): Distribution[Double]

46. def poisson(lambda: Double): Distribution[Int]

47. def sequence[T](ds: List[Distribution[T]]): Distribution[List[T]]

48. def shuffle[A](values: List[A]): Distribution[List[A]]

49. def students_t(df: Int): Distribution[Double]

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

    Definition Classes

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51. def tf(p: Double = 0.5): Distribution[Boolean]

52. def toString(): String

    Definition Classes

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53. object uniform extends Distribution[Double]

    Continuous distributions

54. final def wait(): Unit

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

    Definition Classes

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

    Definition Classes

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57. def weibull(l: Double, k: Double): Distribution[Double]

58. def zipf(s: Double, n: Int): Distribution[Int]