Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

5.  def beta(a: Double, b: Double): Stochastic[Double]

   Creates a Beta random variable.

   Creates a Beta random variable.

   a

   positive parameter

   b

   negative parameter

   returns

   a Beta random variable

   Definition Classes

   ContinuousDistributions

6.  implicit def booleanIsInteger(rv: Stochastic[Boolean]): Stochastic[Int]

   Converts a boolean random variable into an integer random variable.

7.  implicit def booleansHaveMoments[A](rv: Stochastic[A])(implicit f: (Stochastic[A]) ⇒ Stochastic[Int]): Moments[Int]

   Adds moment calculation methods to boolean random variables.

   Adds moment calculation methods to boolean random variables.

   See moments.Moments. This method is needed because of double implicit resolution.

8.  def choose[A](distribution: Seq[(A, Prob)])(implicit ev: (DenseVector[Double]) ⇒ QuasiTensor[Int, Double], sumImpl: breeze.linalg.sum.Impl[DenseVector[Double], Double]): Stochastic[A]

   Creates a random variable that samples from the discrete distribution given as input.

   Creates a random variable that samples from the discrete distribution given as input.

   A

   the concrete type of the random variable

   distribution

   a discrete distribution given as a (finite) sequence of (value, probability) pairs

   ev

   Breeze internals

   sumImpl

   Breeze internals

   returns

   a random variable that samples from the discrete distribution given as input

   Definition Classes

   DiscreteDistributions

9.  def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

10.  def coin: Stochastic[Boolean]

    See coin.

    See coin.

    Definition Classes

    DiscreteDistributions

11.  def coin(p: Prob = 0.5): Stochastic[Boolean]

    Creates a Bernoulli random variable.

    Creates a Bernoulli random variable.

    p

    the probability of a positive outcome

    returns

    a Bernoulli random variable

    Definition Classes

    DiscreteDistributions

12.  def constant[A](a: A): Stochastic[A]

    Creates a random variable that is constant.

    Creates a random variable that is constant.

    A

    the concrete type of this random variable

    a

    the value of this constant random variable

    returns

    a constant random variable

    Definition Classes

    Distributions

13.  def continuousUniform(a: Double, b: Double): Stochastic[Double]

    Creates a uniform random variable on [a, b].

    Creates a uniform random variable on [a, b].

    a

    a first bound on the interval

    b

    a second bound on the interval

    returns

    a uniform random variable on [a, b]

    Definition Classes

    ContinuousDistributions

14.  def continuousUniform: Stochastic[Double]

    Creates a uniform random variable on [0, 1].

    Creates a uniform random variable on [0, 1].

    returns

    a uniform random variable on [0, 1]

    Definition Classes

    ContinuousDistributions

15.  def discreteUniform(n: Int): Stochastic[Int]

    Creates a random variable that samples uniformly from 0 to n excluded.

    Creates a random variable that samples uniformly from 0 to n excluded.

    n

    the number of values that the random variables can take

    returns

    a random variable that samples uniformly from 0 to n excluded

    Definition Classes

    DiscreteDistributions

16.  def discreteUniform[A](values: Seq[A]): Stochastic[A]

    Creates a random variable that samples uniformly from the values given as input.

    Creates a random variable that samples uniformly from the values given as input.

    A

    the concrete type of the random variable

    values

    values from which to sample

    returns

    a random variable that samples uniformly from the values given as input

    Definition Classes

    DiscreteDistributions

17.  implicit def doublesHaveMoments(rv: Stochastic[Double]): Moments[Double]

    Adds specialized moment calculation methods to double random variables.

    Adds specialized moment calculation methods to double random variables.

    See moments.Moments.

18.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

19.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

20.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

21.  def fromMass(mass: Seq[Prob]): Stochastic[Int]

    Creates a random variable that samples from the first natural numbers with probabilities given by mass.

    Creates a random variable that samples from the first natural numbers with probabilities given by mass.

    mass

    a discrete distribution on the first natural numbers given as probabilities

    returns

    a random variable that samples from the first natural numbers with probabilities given by mass

    Definition Classes

    DiscreteDistributions

22.  def gaussian(mean: Double, std: Double): Stochastic[Double]

    Creates a normal random variable.

    Creates a normal random variable.

    mean

    the expectation

    std

    the standard deviation

    returns

    a normal random variable

    Definition Classes

    ContinuousDistributions

23.  def gaussian: Stochastic[Double]

    Creates a standard normal random variable.

    Creates a standard normal random variable.

    returns

    a standard normal random variable

    Definition Classes

    ContinuousDistributions

24.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

25.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

26.  implicit def intsHaveMoments(rv: Stochastic[Int]): Moments[Int]

    Adds specialized moment calculation methods to integer random variables.

    Adds specialized moment calculation methods to integer random variables.

    See moments.Moments.

27.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

28.  def maxEntropy[A](init: Stochastic[A], inverseTemp: Double, burnIn: Int = defaultSampleBurnIn, interval: Int = defaultSampleInterval)(costFunction: (A) ⇒ Double)(symmetricTransitionFunction: (A) ⇒ Stochastic[A]): Stochastic[A]

    Creates a random variable sampled from the maximum entropy distribution of costFunction at inverse temperature inverseTemp.

    Creates a random variable sampled from the maximum entropy distribution of costFunction at inverse temperature inverseTemp.

    A

    the concrete type of the random variable

    init

    the first term of the Markov chain

    burnIn

    the number of initial terms of the chain that are thrown away

    interval

    the number of terms of the chain between two samples

    costFunction

    a function that gives the cost of every possible value of the random variable

    symmetricTransitionFunction

    a function that creates the next term of the Markov chain from the value of the previous one. Jumping back and forth should have the same probability

    returns

    a random variable with the maximum entropy distribution of costFunction at inverse temperature inverseTemp

    Definition Classes

    MetropolisDistributions

29.  def metropolis[A](init: Stochastic[A], burnIn: Int = defaultSampleBurnIn, interval: Int = defaultSampleInterval)(logUnnormalizedProbabilityOf: (A) ⇒ Double)(symmetricTransitionFunction: (A) ⇒ Stochastic[A]): Stochastic[A]

    Creates a random variable that samples from a Metropolis procedure.

    Creates a random variable that samples from a Metropolis procedure.

    A Metropolis procedure is simply a Metropolis-Hastings procedure with a symmetric transition function.

    If the Markov chain converges properly, the random variable will have a distribution given by logUnnormalizedProbabilityOf.

    A

    the concrete type of the random variable

    init

    the first term of the Markov chain

    burnIn

    the number of initial terms of the chain that are thrown away

    interval

    the number of terms of the chain between two samples

    logUnnormalizedProbabilityOf

    the unnormalized log density of the target distribution

    symmetricTransitionFunction

    a function that creates the next term of the Markov chain from the value of the previous one. Jumping back and forth should have the same probability

    returns

    a random variable whose distribution is given by logUnnormalizedProbabilityOf

    Definition Classes

    MetropolisDistributions

30.  def metropolisHastings[A](init: Stochastic[A], burnIn: Int = defaultSampleBurnIn, interval: Int = defaultSampleInterval)(logUnnormalizedProbabilityOf: (A) ⇒ Double)(logTransitionFunction: (A) ⇒ Stochastic[(A, Double, Double)]): Stochastic[A]

    Creates a random variable that samples from a Metropolis-Hastings procedure.

    Creates a random variable that samples from a Metropolis-Hastings procedure.

    If the Markov chain converges properly, the random variable will have a distribution given by logUnnormalizedProbabilityOf.

    A

    the concrete type of the random variable

    init

    the first term of the Markov chain

    burnIn

    the number of initial terms of the chain that are thrown away

    interval

    the number of terms of the chain between two samples

    logUnnormalizedProbabilityOf

    the unnormalized log density of the target distribution

    logTransitionFunction

    a function that creates the next term of the Markov chain from the value of the previous one. The random variable contains the next value and the log probabilities of transitioning forth and back from the previous value to the next one.

    returns

    a random variable whose distribution is given by logUnnormalizedProbabilityOf

    Definition Classes

    MetropolisDistributions

31.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

32.  final def notify(): Unit

    Definition Classes

    AnyRef

33.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

34.  implicit def numericsHaveMoments[A](rv: Stochastic[A])(implicit numeric: Numeric[A]): Moments[A]

    Adds moment calculation methods to numeric random variables.

    Adds moment calculation methods to numeric random variables.

    See moments.Moments.

35.  def posterior[A, O](prior: Stochastic[A], observations: Seq[O], burnIn: Int = defaultSampleBurnIn, interval: Int = defaultSampleInterval)(likelihood: (A, O) ⇒ Prob): Stochastic[A]

    Creates a random variable representing a posterior distribution.

    Creates a random variable representing a posterior distribution.

    The posterior distribution is built from a prior on the parameter to be inferred, observations, and a generative model of the observations given the parameter (the likelihood).

    A

    the concrete type of the parameter

    O

    the type of the observations

    prior

    the prior distribution

    observations

    the observations

    burnIn

    the number of initial terms of the chain that are thrown away

    interval

    the number of terms of the chain between two samples

    likelihood

    a function giving the likelihood of an observation under a given model

    returns

    a random variable representing a posterior distribution

    Definition Classes

    PosteriorDistributions

36.  def posteriorByLog[A, O](prior: Stochastic[A], observations: Seq[O], burnIn: Int = defaultSampleBurnIn, interval: Int = defaultSampleInterval)(logLikelihood: (A, O) ⇒ Double): Stochastic[A]

    Creates a random variable representing a posterior distribution.

    Creates a random variable representing a posterior distribution.

    The posterior distribution is built from a prior on the parameter to be inferred, observations, and a generative model of the observations given the parameter (the likelihood).

    A

    the concrete type of the parameter

    O

    the type of the observations

    prior

    the prior distribution

    observations

    the observations

    burnIn

    the number of initial terms of the chain that are thrown away

    interval

    the number of terms of the chain between two samples

    logLikelihood

    a function giving the log-likelihood of an observation under a given model

    returns

    a random variable representing a posterior distribution

    Definition Classes

    PosteriorDistributions

37.  val randomGenerator: RandomGenerator

    Definition Classes

    AllDistributions → Distributions

38.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

39.  def toString(): String

    Definition Classes

    AnyRef → Any

40.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

41.  final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

42.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )