 distributions

Visibility
1. Public
2. All

Type Members

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

Chi-Squared distribution with k degrees of freedom.

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

Represents a continuous Distribution.

6. trait Density[T] extends AnyRef

Represents an unnormalized probability distribution.

7. 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.

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

Represents a discrete Distribution.

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

Represents a Gamma distribution.

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

Represents a Gaussian distribution over a single real variable.

13. 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.

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

Trait representing conjugate priors.

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

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

16. trait Moments[Mean, Variance] extends AnyRef

Interface for distributions that can report on some of their moments

17. 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.

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

Negative Binomial Distribution

19. 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.

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

Represents a Polya distribution, a.

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

A Rand that changes based on previous draws.

23. class RandBasis extends AnyRef

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

TODO

27. 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.

Value Members

4. object Dirichlet extends Serializable

Provides several defaults for Dirichlets, one for Arrays and one for Counters.

10. object MarkovChain

Provides methods for doing MCMC.

11. object Multinomial extends Serializable

Provides routines to create Multinomials

14. object Rand extends RandBasis

Provides a number of random generators.