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 InvGamma(shape: Double, scale: Double) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with Product with Serializable
26.  case class InvWishart(df: Int, scale: DenseMatrix[Double]) extends ContinuousDistr[DenseMatrix[Double]] with Moments[DenseMatrix[Double], DenseMatrix[Double]] with Product with Serializable
27.  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

28.  class LevyDistribution extends ApacheContinuousDistribution

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

29.  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.

30.  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

31.  trait Moments[Mean, Variance] extends AnyRef

    Interface for distributions that can report on some of their moments

32.  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.

33.  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.

34.  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

35.  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

36.  trait PdfIsUFunc[U <: UFunc, T, P <: PdfIsUFunc[U, T, P]] extends AnyRef
37.  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.

38.  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

39.  trait Process[T] extends Rand[T]

    A Rand that changes based on previous draws.

40.  trait Rand[+T] extends Serializable

    A trait for monadic distributions.

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

41.  class RandBasis extends Serializable

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

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

    TODO

43.  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

44.  trait SufficientStatistic[T <: SufficientStatistic[T]] extends AnyRef

45.  class ThreadLocalRandomGenerator extends RandomGenerator with Serializable

    TODO

    TODO

    Annotations

    @SerialVersionUID()

46.  class TriangularDistribution extends ApacheContinuousDistribution with Moments[Double, Double]

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

47.  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

48.  class VariableKernelEmpiricalDistribution extends ApacheContinuousDistribution

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

49.  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

50.  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

51.  class WeibullDistribution extends ApacheContinuousDistribution

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

52.  case class Wishart(df: Int, scale: DenseMatrix[Double])(implicit randBasis: RandBasis = Rand) extends ContinuousDistr[DenseMatrix[Double]] with Moments[DenseMatrix[Double], DenseMatrix[Double]] with Product with Serializable
53.  class ZipfDistribution extends ApacheDiscreteDistribution