Packages

t

cardano.metropolis

MetropolisDistributions

trait MetropolisDistributions extends Distributions

This trait implements some distributions revolving around the Metropolis-Hastings sampling procedure.

Self Type
MetropolisDistributions
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
    Attributes
    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
    Definition Classes
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  9. def finalize(): Unit
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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  10. final def getClass(): Class[_]
    Definition Classes
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  11. def hashCode(): Int
    Definition Classes
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  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  13. 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

  14. 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

  15. 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

  16. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. final def notify(): Unit
    Definition Classes
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  18. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  19. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  20. def toString(): String
    Definition Classes
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  21. final def wait(): Unit
    Definition Classes
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    Annotations
    @throws( ... )
  22. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
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    @throws( ... )
  23. final def wait(arg0: Long): Unit
    Definition Classes
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    @throws( ... )

Inherited from Distributions

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