probability

Type Members

abstract class AbstractGaussianRV[T, V, Distr <: ContinuousDistr[T] with Moments[T, V] with HasErrorBars[T]] extends ContinuousRandomVariable[T] with ContinuousRVWithDistr[T, Distr]

Gaussian Random Variable
abstract class AbstractStudentsTRandVar[T, V, Distr <: ContinuousDistr[T] with Moments[T, V] with HasErrorBars[T]] extends ContinuousRandomVariable[T] with ContinuousRVWithDistr[T, Distr]
class ApproxBayesComputation[ConditioningSet, Domain] extends RandomVariable[(ConditioningSet, Domain)] with BayesJointProbabilityScheme[ConditioningSet, Domain, RandomVariable[ConditioningSet], RandomVariable[Domain]]
trait BayesJointProbabilityScheme[Domain1, Domain2, P <: RandomVariable[Domain1], D <: RandomVariable[Domain2]] extends RandomVariable[(Domain1, Domain2)] with JointProbabilityScheme[Domain1, Domain2, P, D]
case class BinomialRV(n: Int, p: Double) extends RandomVariable[Int] with DiscreteDistrRV[Int] with Product with Serializable

A binomial random variable.
A binomial random variable. Simulates the accumulated results of n coin tosses of a loaded coin.
case class BlockedMESNRV(tau: Double, alpha: PartitionedVector, mu: PartitionedVector, sigma: PartitionedPSDMatrix) extends ContinuousRandomVariable[PartitionedVector] with ContinuousDistrRV[PartitionedVector] with Product with Serializable

Created by mandar on 28/02/2017.
class ContinuousDistrMixture[Domain, BaseRV <: ContinuousRVWithDistr[Domain, ContinuousDistr[Domain]]] extends ContinuousRandomVariable[Domain] with ContinuousMixtureRV[Domain, BaseRV] with ContinuousDistrRV[Domain]

A random variable mixture over a continuous domain, having a computable probability distribution
A random variable mixture over a continuous domain, having a computable probability distribution
Domain
Domain over which each mixture component is defined
BaseRV
The type of each mixture component, must be a sub type of ContinuousRVWithDistr
trait ContinuousMeasurableFunc[Domain1, Domain2, +R <: ContinuousRandomVariable[Domain1]] extends ContinuousRandomVariable[Domain2] with MeasurableFunction[Domain1, Domain2, R]
trait ContinuousMixtureRV[Domain, BaseRV <: ContinuousRandomVariable[Domain]] extends ContinuousRandomVariable[Domain] with MixtureRV[Domain, BaseRV]

A random variable mixture over a continuous domain
A random variable mixture over a continuous domain
Domain
Domain over which each mixture component is defined
BaseRV
The type of each mixture component, must be a sub type of ContinuousRandomVariable
trait ContinuousRVWithDistr[Domain, +Distr <: ContinuousDistr[Domain]] extends ContinuousRandomVariable[Domain] with RandomVarWithDistr[Domain, Distr]

A continuous random variable that has an associated probability density function.
A continuous random variable that has an associated probability density function.
Domain
Support/Sample space of the random variable
Distr
Type of the probability density as a subtype of breeze ContinuousDistr
abstract class ContinuousRandomVariable[Domain] extends RandomVariable[Domain]

Random variable defined over a continuous domain.
Random variable defined over a continuous domain.
Domain
The domain over which the random variable takes values.
trait DifferentiableLikelihoodModel[Q] extends LikelihoodModel[Q]
trait DiscreteDistrRV[Domain] extends RandomVariable[Domain] with RandomVarWithDistr[Domain, DiscreteDistr[Domain]]

A random variable which takes discrete or categorical values.
trait DiscreteRVWithDistr[Domain, +Distr <: DiscreteDistr[Domain]] extends RandomVariable[Domain] with RandomVarWithDistr[Domain, Distr]
class EncodedContDistrRV[Domain1, Domain2, Distr <: ContinuousDistr[Domain1]] extends ContinuousRandomVariable[Domain2] with ContinuousRVWithDistr[Domain2, ContinuousDistr[Domain2]]

A random variable which consists of a base random variable in Domain1 and an invertible homeomorphism from Domain1 to Domain2.
A random variable which consists of a base random variable in Domain1 and an invertible homeomorphism from Domain1 to Domain2.
Domain1
Domain of the base random variable
Domain2
Domain of the morphed random variable.
case class GaussianRV(mu: Double, sigma: Double) extends AbstractGaussianRV[Double, Double, UnivariateGaussian] with Product with Serializable

Univariate gaussian random variable
class GenericContinuousMCMC[ConditioningSet, Domain] extends RejectionSamplingScheme[ConditioningSet, Domain, ContinuousDistr[ConditioningSet], ContinuousDistr[Domain], AbstractContinuousDistr[(ConditioningSet, Domain)]]

Monte Carlo based bayesian inference model where the parameter space is known to be continuous and hence represented via a ContinuousDistrRV instance.
Monte Carlo based bayesian inference model where the parameter space is known to be continuous and hence represented via a ContinuousDistrRV instance.
ConditioningSet
The type representing the model parameters
Domain
The type representing the observed data.
trait HasDistribution[Domain] extends Serializable

This trait is used to denote random variables which have an underlying probability density function.
trait IIDContinuousRVDistr[D, +Distr <: ContinuousDistr[D], +R <: ContinuousRVWithDistr[D, Distr]] extends RandomVariable[Stream[D]] with IIDRandomVarDistr[D, Distr, R]

An IID Random variable constructed from a continuous random variable having a defined distribution.
An IID Random variable constructed from a continuous random variable having a defined distribution.
D
Base domain
Distr
A breeze probability density defined over the base domain.
R
A random variable defined over the base domain and having distribution of type Distr
trait IIDDiscreteRVDistr[D, +Distr <: DiscreteDistr[D], +R <: DiscreteRVWithDistr[D, Distr]] extends RandomVariable[Stream[D]] with IIDRandomVarDistr[D, Distr, R]
trait IIDRandomVarDistr[D, +Dist <: Density[D] with Rand[D], +R <: RandomVarWithDistr[D, Dist]] extends RandomVariable[Stream[D]] with IIDRandomVariable[D, R] with RandomVarWithDistr[Stream[D], Density[Stream[D]] with Rand[Stream[D]]]

An i.i.d random variable with a defined distribution.
An i.i.d random variable with a defined distribution.
D
Base domain
Dist
A breeze distribution defined over the base domain.
R
A random variable defined over the base domain and having distribution of type Dist
trait IIDRandomVariable[D, +R <: RandomVariable[D]] extends RandomVariable[Stream[D]]

An independent and identically distributed RandomVariable represented as a Stream
An independent and identically distributed RandomVariable represented as a Stream
D
The base domain
R
Random variable defined over the base domain
trait JointProbabilityScheme[Domain1, Domain2, P <: RandomVariable[Domain1], D <: RandomVariable[Domain2]] extends RandomVariable[(Domain1, Domain2)]

Created by mandar on 09/01/2017.
trait Likelihood[Q, R, S, W] extends AnyRef

Q
The type of observed variable (y)
R
The type of conditioning variable (f)
S
Result type of the hessian method.
W
The type representing a gaussian distribution for f
trait LikelihoodModel[Q] extends DataPipe[Q, Double]
case class MatrixNormalRV(m: DenseMatrix[Double], u: DenseMatrix[Double], v: DenseMatrix[Double]) extends AbstractGaussianRV[DenseMatrix[Double], (DenseMatrix[Double], DenseMatrix[Double]), MatrixNormal] with Product with Serializable

Matrix gaussian random variable
case class MatrixTRV(mu: Double, m: DenseMatrix[Double], u: DenseMatrix[Double], v: DenseMatrix[Double]) extends AbstractStudentsTRandVar[DenseMatrix[Double], (DenseMatrix[Double], DenseMatrix[Double]), MatrixT] with Product with Serializable
class MeasurableDistrRV[Domain1, Domain2, Jacobian, Distr1 <: ContinuousDistr[Domain1]] extends ContinuousRandomVariable[Domain2] with ContinuousMeasurableFunc[Domain1, Domain2, ContinuousRVWithDistr[Domain1, Distr1]] with ContinuousDistrRV[Domain2]

A measurable function of a continuous random variable with a defined probability density function.
A measurable function of a continuous random variable with a defined probability density function.
Domain1
The domain of the base random variable
Domain2
The output set of the function
Jacobian
The type representing the Jacobian of inverse of the map func
trait MeasurableFunction[Domain1, Domain2, +R <: RandomVariable[Domain1]] extends RandomVariable[Domain2]

A measurable function is any mapping/function applied to samples generated by some base random variable instance.
A measurable function is any mapping/function applied to samples generated by some base random variable instance.
Domain1
Type over which the base random variable is defined.
Domain2
Type over which output of function takes its values.
R
The type of the base Random Variable, must inherit from RandomVariable
trait MixtureRV[Domain, BaseRV <: RandomVariable[Domain]] extends RandomVariable[Domain]

Top level class for representing random variable mixtures.
Top level class for representing random variable mixtures.
Domain
Domain over which each mixture component is defined
BaseRV
The type of each mixture component, must be a sub type of RandomVariable
case class MultGaussianPRV(mu: PartitionedVector, covariance: PartitionedPSDMatrix)(implicit ev: Field[PartitionedVector]) extends AbstractGaussianRV[PartitionedVector, PartitionedPSDMatrix, BlockedMultiVariateGaussian] with Product with Serializable

Multivariate blocked gaussian random variable
case class MultGaussianRV(mu: DenseVector[Double], covariance: DenseMatrix[Double])(implicit ev: Field[DenseVector[Double]]) extends AbstractGaussianRV[DenseVector[Double], DenseMatrix[Double], MVGaussian] with Product with Serializable

Multivariate gaussian random variable
case class MultStudentsTPRV(mu: Double, mean: PartitionedVector, covariance: PartitionedPSDMatrix)(implicit ev: Field[PartitionedVector]) extends AbstractStudentsTRandVar[PartitionedVector, PartitionedPSDMatrix, BlockedMultivariateStudentsT] with Product with Serializable
case class MultStudentsTRV(mu: Double, mean: DenseVector[Double], covariance: DenseMatrix[Double])(implicit ev: Field[DenseVector[Double]]) extends AbstractStudentsTRandVar[DenseVector[Double], DenseMatrix[Double], MultivariateStudentsT] with Product with Serializable
case class MultinomialRV(weights: DenseVector[Double]) extends RandomVariable[Int] with DiscreteDistrRV[Int] with Product with Serializable

A multinomial random variable i.e.
A multinomial random variable i.e. draws values between 0 and N-1
trait RandomVarWithDistr[Domain, +Dist <: Density[Domain] with Rand[Domain]] extends RandomVariable[Domain] with HasDistribution[Domain]

A random variable which has a computable probability density.
A random variable which has a computable probability density.
Domain
The set over which the random variable takes its values.
Dist
A breeze probability density defined on the Domain
abstract class RandomVariable[Domain] extends Serializable

Represents a bare bones representation of a random variable only in terms of samples.
Represents a bare bones representation of a random variable only in terms of samples.
Domain
The domain or space over which the random variable is defined; i.e. the range of values it can take
abstract class RejectionSamplingScheme[ConditioningSet, Domain, Dist <: Density[ConditioningSet] with Rand[ConditioningSet], DistL <: Density[Domain] with Rand[Domain], JointDist <: Density[(ConditioningSet, Domain)] with Rand[(ConditioningSet, Domain)]] extends RandomVariable[(ConditioningSet, Domain)] with RandomVarWithDistr[(ConditioningSet, Domain), JointDist] with BayesJointProbabilityScheme[ConditioningSet, Domain, RandomVarWithDistr[ConditioningSet, Dist], RandomVarWithDistr[Domain, DistL]]

Created by mandar on 26/7/16.
case class StudentsTRV(mu: Double, mean: Double, sigma: Double) extends AbstractStudentsTRandVar[Double, Double, UnivariateStudentsT] with Product with Serializable
class ThreadedRandomVariable[Domain] extends RandomVariable[Domain]
case class UniformRV(min: Double, max: Double) extends ContinuousRandomVariable[Double] with ContinuousDistrRV[Double] with Product with Serializable

Created by mandar on 26/7/16.
class VectorIIDProbit extends Likelihood[DenseVector[Double], DenseVector[Double], DenseMatrix[Double], (DenseVector[Double], DenseVector[Double])]

Represents an IID probit likelihood used in Gaussian Process binary classification models
Represents an IID probit likelihood used in Gaussian Process binary classification models
p(y = 1| f) = Φ(yf)
class VectorIIDSigmoid extends Likelihood[DenseVector[Double], DenseVector[Double], DenseMatrix[Double], (DenseVector[Double], DenseVector[Double])]

Represents an IID sigmoid likelihood used in Gaussian Process binary classification models
Represents an IID sigmoid likelihood used in Gaussian Process binary classification models
p(y = 1| f) = 1/(1 + exp(-yf))
trait ContinuousDistrRV[Domain] extends ContinuousRandomVariable[Domain] with ContinuousRVWithDistr[Domain, ContinuousDistr[Domain]]

A continuous random variable that has an associated probability density function.
A continuous random variable that has an associated probability density function.

Annotations
@deprecated
Deprecated
ContinuousDistrRV is deprecated as of DynaML v1.5, prefer ContinuousRVWithDistr

Value Members

object ApproxBayesComputation extends Serializable
object ContinuousDistrMixture extends Serializable

Contains convenience methods for creating various mixture random variable instances.
object ContinuousMeasurableFunc extends Serializable
object DifferentiableLikelihoodModel extends Serializable
def E(rv: RandomVariable[Double]): Double
def E[I](rv: RandomVariable[I])(implicit f: InnerProductSpace[I, Double]): I

Calculate the monte carlo estimate of the first moment/expectation of a random variable.
Calculate the monte carlo estimate of the first moment/expectation of a random variable.
I
The domain of the random variable.
rv
The random variable from which to sample
f
An implicit object representing an inner product on I
object EncodedContDistrRV extends Serializable
object IIDContinuousRVDistr extends Serializable
object IIDDiscreteRVDistr extends Serializable
object IIDRandomVarDistr extends Serializable
object IIDRandomVariable extends Serializable
def KL[I, Distr <: ContinuousDistr[I]](p: ContinuousRVWithDistr[I, Distr])(q: ContinuousRVWithDistr[I, Distr]): Double

KL divergence:
KL divergence:
p
The base random variable
q
The random variable used to approximate p
returns
The Kulback Leibler divergence KL(P||Q)
object LikelihoodModel extends Serializable
object MeasurableFunction extends Serializable
object MixtureRV extends Serializable
object MultGaussianPRV extends Serializable
object MultGaussianRV extends Serializable
object MultStudentsTPRV extends Serializable
object MultStudentsTRV extends Serializable
def OrderStats(r: ContinuousRVWithDistr[Double, ContinuousDistr[Double]], alpha: Double): (Double, Double, Double, Double, Double)

Calculate the (monte carlo approximation to) mean, median, mode, lower and upper confidence interval.
Calculate the (monte carlo approximation to) mean, median, mode, lower and upper confidence interval.
r
Continuous random variable in question
alpha
Probability of exclusion, i.e. return 100(1-alpha) % confidence interval. alpha should be between 0 and 1
object RandomVariable extends Serializable

Companion object of the RandomVariable class.
Companion object of the RandomVariable class. Contains apply methods which can create random variables.
var candidates: Int

Number of monte carlo samples to generate.
package distributions
def entropy[I, Distr <: ContinuousDistr[I]](rv: ContinuousRVWithDistr[I, Distr]): Double

Calculate the entropy of a random variable
package mcmc

package probability

Type Members

abstract class AbstractGaussianRV[T, V, Distr <: ContinuousDistr[T] with Moments[T, V] with HasErrorBars[T]] extends ContinuousRandomVariable[T] with ContinuousRVWithDistr[T, Distr]

Gaussian Random Variable

abstract class AbstractStudentsTRandVar[T, V, Distr <: ContinuousDistr[T] with Moments[T, V] with HasErrorBars[T]] extends ContinuousRandomVariable[T] with ContinuousRVWithDistr[T, Distr]

class ApproxBayesComputation[ConditioningSet, Domain] extends RandomVariable[(ConditioningSet, Domain)] with BayesJointProbabilityScheme[ConditioningSet, Domain, RandomVariable[ConditioningSet], RandomVariable[Domain]]

trait BayesJointProbabilityScheme[Domain1, Domain2, P <: RandomVariable[Domain1], D <: RandomVariable[Domain2]] extends RandomVariable[(Domain1, Domain2)] with JointProbabilityScheme[Domain1, Domain2, P, D]

case class BinomialRV(n: Int, p: Double) extends RandomVariable[Int] with DiscreteDistrRV[Int] with Product with Serializable

case class BlockedMESNRV(tau: Double, alpha: PartitionedVector, mu: PartitionedVector, sigma: PartitionedPSDMatrix) extends ContinuousRandomVariable[PartitionedVector] with ContinuousDistrRV[PartitionedVector] with Product with Serializable

class ContinuousDistrMixture[Domain, BaseRV <: ContinuousRVWithDistr[Domain, ContinuousDistr[Domain]]] extends ContinuousRandomVariable[Domain] with ContinuousMixtureRV[Domain, BaseRV] with ContinuousDistrRV[Domain]

trait ContinuousMeasurableFunc[Domain1, Domain2, +R <: ContinuousRandomVariable[Domain1]] extends ContinuousRandomVariable[Domain2] with MeasurableFunction[Domain1, Domain2, R]

trait ContinuousMixtureRV[Domain, BaseRV <: ContinuousRandomVariable[Domain]] extends ContinuousRandomVariable[Domain] with MixtureRV[Domain, BaseRV]

trait ContinuousRVWithDistr[Domain, +Distr <: ContinuousDistr[Domain]] extends ContinuousRandomVariable[Domain] with RandomVarWithDistr[Domain, Distr]

abstract class ContinuousRandomVariable[Domain] extends RandomVariable[Domain]

trait DifferentiableLikelihoodModel[Q] extends LikelihoodModel[Q]

trait DiscreteDistrRV[Domain] extends RandomVariable[Domain] with RandomVarWithDistr[Domain, DiscreteDistr[Domain]]

trait DiscreteRVWithDistr[Domain, +Distr <: DiscreteDistr[Domain]] extends RandomVariable[Domain] with RandomVarWithDistr[Domain, Distr]

class EncodedContDistrRV[Domain1, Domain2, Distr <: ContinuousDistr[Domain1]] extends ContinuousRandomVariable[Domain2] with ContinuousRVWithDistr[Domain2, ContinuousDistr[Domain2]]

case class GaussianRV(mu: Double, sigma: Double) extends AbstractGaussianRV[Double, Double, UnivariateGaussian] with Product with Serializable

class GenericContinuousMCMC[ConditioningSet, Domain] extends RejectionSamplingScheme[ConditioningSet, Domain, ContinuousDistr[ConditioningSet], ContinuousDistr[Domain], AbstractContinuousDistr[(ConditioningSet, Domain)]]

trait HasDistribution[Domain] extends Serializable

trait IIDContinuousRVDistr[D, +Distr <: ContinuousDistr[D], +R <: ContinuousRVWithDistr[D, Distr]] extends RandomVariable[Stream[D]] with IIDRandomVarDistr[D, Distr, R]

trait IIDDiscreteRVDistr[D, +Distr <: DiscreteDistr[D], +R <: DiscreteRVWithDistr[D, Distr]] extends RandomVariable[Stream[D]] with IIDRandomVarDistr[D, Distr, R]

trait IIDRandomVarDistr[D, +Dist <: Density[D] with Rand[D], +R <: RandomVarWithDistr[D, Dist]] extends RandomVariable[Stream[D]] with IIDRandomVariable[D, R] with RandomVarWithDistr[Stream[D], Density[Stream[D]] with Rand[Stream[D]]]

trait IIDRandomVariable[D, +R <: RandomVariable[D]] extends RandomVariable[Stream[D]]

trait JointProbabilityScheme[Domain1, Domain2, P <: RandomVariable[Domain1], D <: RandomVariable[Domain2]] extends RandomVariable[(Domain1, Domain2)]

trait Likelihood[Q, R, S, W] extends AnyRef

trait LikelihoodModel[Q] extends DataPipe[Q, Double]

case class MatrixNormalRV(m: DenseMatrix[Double], u: DenseMatrix[Double], v: DenseMatrix[Double]) extends AbstractGaussianRV[DenseMatrix[Double], (DenseMatrix[Double], DenseMatrix[Double]), MatrixNormal] with Product with Serializable

case class MatrixTRV(mu: Double, m: DenseMatrix[Double], u: DenseMatrix[Double], v: DenseMatrix[Double]) extends AbstractStudentsTRandVar[DenseMatrix[Double], (DenseMatrix[Double], DenseMatrix[Double]), MatrixT] with Product with Serializable

class MeasurableDistrRV[Domain1, Domain2, Jacobian, Distr1 <: ContinuousDistr[Domain1]] extends ContinuousRandomVariable[Domain2] with ContinuousMeasurableFunc[Domain1, Domain2, ContinuousRVWithDistr[Domain1, Distr1]] with ContinuousDistrRV[Domain2]

trait MeasurableFunction[Domain1, Domain2, +R <: RandomVariable[Domain1]] extends RandomVariable[Domain2]

trait MixtureRV[Domain, BaseRV <: RandomVariable[Domain]] extends RandomVariable[Domain]

case class MultGaussianPRV(mu: PartitionedVector, covariance: PartitionedPSDMatrix)(implicit ev: Field[PartitionedVector]) extends AbstractGaussianRV[PartitionedVector, PartitionedPSDMatrix, BlockedMultiVariateGaussian] with Product with Serializable

case class MultGaussianRV(mu: DenseVector[Double], covariance: DenseMatrix[Double])(implicit ev: Field[DenseVector[Double]]) extends AbstractGaussianRV[DenseVector[Double], DenseMatrix[Double], MVGaussian] with Product with Serializable

case class MultStudentsTPRV(mu: Double, mean: PartitionedVector, covariance: PartitionedPSDMatrix)(implicit ev: Field[PartitionedVector]) extends AbstractStudentsTRandVar[PartitionedVector, PartitionedPSDMatrix, BlockedMultivariateStudentsT] with Product with Serializable

case class MultStudentsTRV(mu: Double, mean: DenseVector[Double], covariance: DenseMatrix[Double])(implicit ev: Field[DenseVector[Double]]) extends AbstractStudentsTRandVar[DenseVector[Double], DenseMatrix[Double], MultivariateStudentsT] with Product with Serializable

case class MultinomialRV(weights: DenseVector[Double]) extends RandomVariable[Int] with DiscreteDistrRV[Int] with Product with Serializable

trait RandomVarWithDistr[Domain, +Dist <: Density[Domain] with Rand[Domain]] extends RandomVariable[Domain] with HasDistribution[Domain]

abstract class RandomVariable[Domain] extends Serializable

case class StudentsTRV(mu: Double, mean: Double, sigma: Double) extends AbstractStudentsTRandVar[Double, Double, UnivariateStudentsT] with Product with Serializable

class ThreadedRandomVariable[Domain] extends RandomVariable[Domain]

case class UniformRV(min: Double, max: Double) extends ContinuousRandomVariable[Double] with ContinuousDistrRV[Double] with Product with Serializable

class VectorIIDProbit extends Likelihood[DenseVector[Double], DenseVector[Double], DenseMatrix[Double], (DenseVector[Double], DenseVector[Double])]

class VectorIIDSigmoid extends Likelihood[DenseVector[Double], DenseVector[Double], DenseMatrix[Double], (DenseVector[Double], DenseVector[Double])]

trait ContinuousDistrRV[Domain] extends ContinuousRandomVariable[Domain] with ContinuousRVWithDistr[Domain, ContinuousDistr[Domain]]

Value Members

object ApproxBayesComputation extends Serializable

object ContinuousDistrMixture extends Serializable

object ContinuousMeasurableFunc extends Serializable

object DifferentiableLikelihoodModel extends Serializable

def E(rv: RandomVariable[Double]): Double

def E[I](rv: RandomVariable[I])(implicit f: InnerProductSpace[I, Double]): I

object EncodedContDistrRV extends Serializable

object IIDContinuousRVDistr extends Serializable

object IIDDiscreteRVDistr extends Serializable

object IIDRandomVarDistr extends Serializable

object IIDRandomVariable extends Serializable

def KL[I, Distr <: ContinuousDistr[I]](p: ContinuousRVWithDistr[I, Distr])(q: ContinuousRVWithDistr[I, Distr]): Double

object LikelihoodModel extends Serializable

object MeasurableFunction extends Serializable

object MixtureRV extends Serializable

object MultGaussianPRV extends Serializable

object MultGaussianRV extends Serializable

object MultStudentsTPRV extends Serializable

object MultStudentsTRV extends Serializable

def OrderStats(r: ContinuousRVWithDistr[Double, ContinuousDistr[Double]], alpha: Double): (Double, Double, Double, Double, Double)

object RandomVariable extends Serializable

var candidates: Int

package distributions

def entropy[I, Distr <: ContinuousDistr[I]](rv: ContinuousRVWithDistr[I, Distr]): Double

package mcmc

Inherited from AnyRef

Inherited from Any

Ungrouped