Packages

t

cardano.bayesian

PosteriorDistributions

trait PosteriorDistributions extends Distributions

This trait implements calculation of posterior distributions using Markov chain Monte-Carlo techniques.

Self Type
PosteriorDistributions
Linear Supertypes
Distributions, AnyRef, Any
Known Subclasses
AllDistributions, Stochastic
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Abstract Value Members

  1. abstract def randomGenerator: RandomGenerator
    Attributes
    protected
    Definition Classes
    Distributions

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[java.lang]
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    @throws( ... )
  6. 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
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: Any): Boolean
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  9. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] )
  10. final def getClass(): Class[_]
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  11. def hashCode(): Int
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  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
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  13. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  14. final def notify(): Unit
    Definition Classes
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  15. final def notifyAll(): Unit
    Definition Classes
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  16. 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

  17. 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

  18. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  19. def toString(): String
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  20. final def wait(): Unit
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    @throws( ... )
  21. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  22. final def wait(arg0: Long): Unit
    Definition Classes
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    @throws( ... )

Inherited from Distributions

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