distributions

Type Members

1. class Bernoulli extends DiscreteDistr[Boolean] with Moments[Double]

2. class Beta extends ContinuousDistr[Double] with Moments[Double]

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

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

4. trait ContinuousDistr[T] extends Measure[T] with Rand[T]

   Represents a continuous Distribution.

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

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

6. trait DiscreteDistr[T] extends Measure[T] with Rand[T]

   Represents a discrete Distribution.

7. case class Exponential(rate: Double)(implicit basis: RandBasis = Rand) extends ContinuousDistr[Double] with Product with Serializable

8. trait ExponentialFamily[D, T] extends AnyRef

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

   Represents a Gamma distribution.

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

    Represents a Gaussian distribution over a single real variable.

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

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

12. trait HasConjugatePrior[Likelihood <: Measure[T], T] extends ExponentialFamily[Likelihood, T]

    Trait representing conjugate priors.

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

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

14. trait Measure[T] extends AnyRef

    Represents an unnormalized probability distribution.

15. trait Moments[T] extends AnyRef

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

    Represents a Multinomial distribution over elements.

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

    Negative Binomial Distribution

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

    Represents a Poisson random variable.

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

    Represents a Polya distribution, a.

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

    A Rand that changes based on previous draws.

21. trait Rand[+T] extends AnyRef

    A trait for monadic distributions.

22. class RandBasis extends AnyRef

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

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

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

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

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