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object Kernels

Provides Markov transition kernels for a few common MCMC techniques

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  1. final def !=(arg0: Any): Boolean
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  12. def metropolis[T](proposal: (T) ⇒ Rand[T])(logMeasure: (T) ⇒ Double)(implicit rand: RandBasis = Rand): (T) ⇒ Rand[T]

    Note this is not Metropolis-Hastings

    Note this is not Metropolis-Hastings

    proposal

    the symmetric proposal distribution generator

    logMeasure

    the distribution we want to sample from

  13. def metropolisHastings[T](proposal: (T) ⇒ ContinuousDistr[T])(logMeasure: (T) ⇒ Double)(implicit rand: RandBasis = Rand): (T) ⇒ Rand[T]

    proposal

    the proposal distribution generator

    logMeasure

    the distribution we want to sample from

  14. final def ne(arg0: AnyRef): Boolean
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  15. final def notify(): Unit
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  16. final def notifyAll(): Unit
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  17. def slice(logMeasure: (Double) ⇒ Double, valid: (Double) ⇒ Boolean)(implicit rand: RandBasis = Rand): (Double) ⇒ Rand[Double]

    Creates a slice sampler for a function.

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

    logMeasure

    an unnormalized probability measure

    returns

    a slice sampler

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