distributions

Type Members

1. case class AliasTable[I](probs: DenseVector[Double], aliases: DenseVector[Int], outcomes: IndexedSeq[I], rand: RandBasis) extends Product with Serializable

   

2. trait ApacheContinuousDistribution extends ContinuousDistr[Double] with HasCdf with HasInverseCdf

   

3. trait ApacheDiscreteDistribution extends DiscreteDistr[Int]

   

4. class Bernoulli extends DiscreteDistr[Boolean] with Moments[Double, Double]

   

5. class Beta extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf

   

   The Beta distribution, which is the conjugate prior for the Bernoulli distribution

6. case class Binomial(n: Int, p: Double)(implicit rand: RandBasis = Rand) extends DiscreteDistr[Int] with Moments[Double, Double] with Product with Serializable

   

   A binomial distribution returns how many coin flips out of n are heads, where numYes is the probability of any one coin being heads.

   A binomial distribution returns how many coin flips out of n are heads, where numYes is the probability of any one coin being heads.

   n

   is the number of coin flips

   p

   the probability of any one being true

7. class CauchyDistribution extends ApacheContinuousDistribution

   

   The Cauchy-distribution

8. case class ChiSquared(k: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf with Product with Serializable

   

   Chi-Squared distribution with k degrees of freedom.

9. trait ContinuousDistr[T] extends Density[T] with Rand[T]

   

   Represents a continuous Distribution.

   Represents a continuous Distribution. Why T? just in case.

10. trait ContinuousDistributionUFuncProvider[T, D <: ContinuousDistr[T]] extends UFunc with MappingUFunc

    

11. trait Density[T] extends AnyRef

    

    Represents an unnormalized probability distribution.

12. case class Dirichlet[T, I](params: T)(implicit space: EnumeratedCoordinateField[T, I, Double], rand: RandBasis = Rand) extends ContinuousDistr[T] with Product with Serializable

    

    Represents a Dirichlet distribution, the conjugate prior to the multinomial.

13. trait DiscreteDistr[T] extends Density[T] with Rand[T]

    

    Represents a discrete Distribution.

14. case class Exponential(rate: Double)(implicit basis: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf with Product with Serializable

    

15. trait ExponentialFamily[D, T] extends AnyRef

    

16. class FDistribution extends ApacheContinuousDistribution

    

    The F-distribution - ratio of two scaled chi^2 variables

17. case class Gamma(shape: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf with Product with Serializable

    

    Represents a Gamma distribution.

    Represents a Gamma distribution. E[X] = shape * scale

18. case class Gaussian(mu: Double, sigma: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf with Product with Serializable

    

    Represents a Gaussian distribution over a single real variable.

19. case class Geometric(p: Double)(implicit rand: RandBasis = Rand) extends DiscreteDistr[Int] with Moments[Double, Double] with Product with Serializable

    

    The Geometric distribution calculates the number of trials until the first success, which happens with probability p.

20. case class Gumbel(location: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with Product with Serializable

    

    TODO

21. trait HasCdf extends AnyRef

    

22. trait HasConjugatePrior[Likelihood <: Density[T], T] extends ExponentialFamily[Likelihood, T]

    

    Trait representing conjugate priors.

    Trait representing conjugate priors. See Dirichlet for an example.

23. trait HasInverseCdf extends AnyRef

    

24. class HypergeometricDistribution extends ApacheDiscreteDistribution

    

    The Hypergeometric-distribution - ratio of two scaled chi^2 variables

25. case class Laplace(location: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with Product with Serializable

    

    http://en.wikipedia.org/wiki/Laplace_distribution

26. class LevyDistribution extends ApacheContinuousDistribution

    

    The Levy-distribution - ratio of two scaled chi^2 variables

27. case class LogNormal(mu: Double, sigma: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf with Product with Serializable

    

    A log normal distribution is distributed such that log X ~ Normal(\mu, \sigma)

    A log normal distribution is distributed such that log X ~ Normal(\mu, \sigma)

    TODO: it should be possible to specify distributions like this by using an breeze.util.Isomorphism instances.

28. case class Logarthmic(p: Double)(implicit rand: RandBasis = Rand) extends DiscreteDistr[Int] with Moments[Double, Double] with Product with Serializable

    

    The Logarithmic distribution

    The Logarithmic distribution

    http://en.wikipedia.org/wiki/Logarithmic_distribution

29. trait Moments[Mean, Variance] extends AnyRef

    

    Interface for distributions that can report on some of their moments

30. case class Multinomial[T, I](params: T)(implicit ev: (T) ⇒ QuasiTensor[I, Double], sumImpl: linalg.sum.Impl[T, Double], rand: RandBasis = Rand) extends DiscreteDistr[I] with Product with Serializable

    

    Represents a Multinomial distribution over elements.

    Represents a Multinomial distribution over elements. You can make a distribution over any breeze.linalg.QuasiTensor, which includes DenseVectors and Counters.

    TODO: I should probably rename this to Discrete or something, since it only handles one draw.

31. case class MultivariateGaussian(mean: DenseVector[Double], covariance: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseVector[Double]] with Moments[DenseVector[Double], DenseMatrix[Double]] with Product with Serializable

    

    Represents a Gaussian distribution over a single real variable.

32. case class NegativeBinomial(r: Double, p: Double) extends DiscreteDistr[Int] with Product with Serializable

    

    Negative Binomial Distribution

    Negative Binomial Distribution

    r

    number of failures until stop

    p

    prob of success

33. case class Pareto(scale: Double, shape: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with Product with Serializable

    

    http://en.wikipedia.org/wiki/Laplace_distribution

34. trait PdfIsUFunc[U <: UFunc, T, P <: PdfIsUFunc[U, T, P]] extends AnyRef

    

35. case class Poisson(mean: Double)(implicit rand: RandBasis = Rand) extends DiscreteDistr[Int] with Moments[Double, Double] with Product with Serializable

    

    Represents a Poisson random variable.

36. class Polya[T, I] extends DiscreteDistr[I]

    

    Represents a Polya distribution, a.k.a Dirichlet compound Multinomial distribution see http://en.wikipedia.org/wiki/Multivariate_Polya_distribution

37. trait Process[T] extends Rand[T]

    

    A Rand that changes based on previous draws.

38. trait Rand[+T] extends Serializable

    

    A trait for monadic distributions.

    A trait for monadic distributions. Provides support for use in for-comprehensions

39. class RandBasis extends Serializable

    

    Provides standard combinators and such to use to compose new Rands.

40. case class Rayleigh(scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with Product with Serializable

    

    TODO

41. case class StudentsT(degreesOfFreedom: Double)(implicit randBasis: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with Product with Serializable

    

    Implements Student's T distribution http://en.wikipedia.org/wiki/Student's_t-distribution

42. trait SufficientStatistic[T <: SufficientStatistic[T]] extends AnyRef

    

43. class ThreadLocalRandomGenerator extends RandomGenerator

    

    TODO

44. class TriangularDistribution extends ApacheContinuousDistribution with Moments[Double, Double]

    

    The Triangular-distribution - ratio of two scaled chi^2 variables

45. case class Uniform(low: Double, high: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf with Product with Serializable

    

46. class VariableKernelEmpiricalDistribution extends ApacheContinuousDistribution

    

    The Weibull-distribution - ratio of two scaled chi^2 variables

47. case class VonMises(mu: Double, k: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with Product with Serializable

    

    Represents a Von Mises distribution, which is a distribution over angles.

    Represents a Von Mises distribution, which is a distribution over angles.

    mu

    is the mean of the distribution, ~ gaussian mean

    k

    is the concentration, which is like 1/gaussian variance

48. case class Wald(mean: Double, shape: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with Product with Serializable

    

    Also known as the inverse Gaussian Distribution

    Also known as the inverse Gaussian Distribution

    http://en.wikipedia.org/wiki/Inverse_Gaussian_distribution

49. class WeibullDistribution extends ApacheContinuousDistribution

    

    The Weibull-distribution - ratio of two scaled chi^2 variables

50. class ZipfDistribution extends ApacheDiscreteDistribution