object MarkovChain

Provides methods for doing MCMC.

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  4. 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

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  13. 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

  14. def metropolisHastings[T](init: T, proposal: (T) ⇒ ContinuousDistr[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

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  18. 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

  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  24. 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]

  25. object Kernels

    Provides Markov transition kernels for a few common MCMC techniques

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