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.  case class Bernoulli(p: Double)(implicit rand: RandBasis = Rand) extends DiscreteDistr[Boolean] with Moments[Double, Double] with Product with Serializable

   A Bernoulli distribution represents a distribution over weighted coin flips

   A Bernoulli distribution represents a distribution over weighted coin flips

   p

   the probability of true

5.  case class Beta(a: Double, b: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf with Product with Serializable

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

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

   a

   the number of pseudo-observations for true

   b

   the number of pseudo-observations for false

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.  case class CauchyDistribution(median: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ApacheContinuousDistribution with Product with Serializable

   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.  case class FDistribution(numeratorDegreesOfFreedom: Double, denominatorDegreesOfFreedom: Double) extends ApacheContinuousDistribution with Product with Serializable

    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.  case class LevyDistribution(mu: Double, c: Double, generator: RandomGenerator = new JDKRandomGenerator()) extends ApacheContinuousDistribution with Product with Serializable

    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.  case class WeibullDistribution(alpha: Double, beta: Double) extends ApacheContinuousDistribution with Product with Serializable

    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.  case class ZipfDistribution(numberOfElements: Int, exponent: Double) extends ApacheDiscreteDistribution with Product with Serializable