MarkovChain

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. object Combinators

   

   Combinators for creating transition kernels from other kernels or things that are not quite transition kernels.

   Combinators for creating transition kernels from other kernels or things that are not quite transition kernels. A kernel is a fn of type T=<Rand[T]

5. object Kernels

   

   Provides Markov transition kernels for a few common MCMC techniques

6. def apply[T](init: T)(resample: (T) ⇒ Rand[T]): Process[T]

   

   Given an initial state and an arbitrary Markov transition, return a sampler for doing mcmc

7. final def asInstanceOf[T0]: T0

   

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8. def clone(): AnyRef

   

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9. final def eq(arg0: AnyRef): Boolean

   

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10. def equals(arg0: Any): Boolean

    

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11. def finalize(): Unit

    

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12. final def getClass(): Class[_]

    

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13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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15. def metropolis[T](init: T, proposal: (T) ⇒ Rand[T])(logMeasure: (T) ⇒ Double): Process[T]

    

    Performs Metropolis distributions on a random variable.

    Performs Metropolis distributions on a random variable. Note this is not Metropolis-Hastings

    init

    The initial parameter

    proposal

    the symmetric proposal distribution generator

    logMeasure

    the distribution we want to sample from

16. def metropolisHastings[T](init: T, proposal: (T) ⇒ Density[T] with Rand[T])(logMeasure: (T) ⇒ Double): Process[T]

    

    Performs Metropolis-Hastings distributions on a random variable.

    Performs Metropolis-Hastings distributions on a random variable.

    init

    The initial parameter

    proposal

    the proposal distribution generator

    logMeasure

    the distribution we want to sample from

17. final def ne(arg0: AnyRef): Boolean

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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20. def slice(init: Double, logMeasure: (Double) ⇒ Double, valid: (Double) ⇒ Boolean): Process[Double]

    

    Creates a slice sampler for a function.

    Creates a slice sampler for a function. logMeasure should be an (unnormalized) log pdf.

    init

    guess

    logMeasure

    an unnormalized probability measure

    returns

    a slice sampler

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

    

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22. def toString(): String

    

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23. final def wait(): Unit

    

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

    

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25. final def wait(arg0: Long): Unit

    

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