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 with HasInverseCdf

   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)(implicit rand: RandBasis = Rand) 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