# 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.