ParticleMetropolisHastings

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. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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   protected[java.lang]

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

   

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7. def finalize(): Unit

   

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8. def fromProcess[F[_], A](iter: Process[A])(implicit arg0: Suspendable[F]): Stream[F, A]

   

   Definition Classes

   MetropolisHastings

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

   

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10. val initialParams: Parameters

    

    the starting parameters for the metropolis algorithm

    the starting parameters for the metropolis algorithm

    Definition Classes

    ParticleMetropolisHastings → MetropolisHastings

11. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

12. def iters[F[_]](implicit arg0: Suspendable[F]): Stream[F, MetropState]

    

    Use the same step for iterations in a stream

    Use the same step for iterations in a stream

    Definition Classes

    MetropolisHastings

13. val logLikelihood: (Parameters) ⇒ LogLikelihood

    

    a function from parameters to LogLikelihood

    a function from parameters to LogLikelihood

    Definition Classes

    ParticleMetropolisHastings → MetropolisHastings

14. def logTransition(from: Parameters, to: Parameters): LogLikelihood

    

    Definition of the log-transition, used when calculating the acceptance ratio This is the probability of moving between parameters according to the proposal distribution Note: When using a symmetric proposal distribution (eg.

    Definition of the log-transition, used when calculating the acceptance ratio This is the probability of moving between parameters according to the proposal distribution Note: When using a symmetric proposal distribution (eg. Normal) this cancels in the acceptance ratio

    from

    the previous parameter value

    to

    the proposed parameter value

    Definition Classes

    ParticleMetropolisHastings → MetropolisHastings

15. def markovIters: Process[MetropState]

    

    Use the Breeze Markov Chain to generate a process of MetropState Calling .sample(n) on this will create a single site metropolis hastings, proposing parameters only from the initial supplied parameter values

    Use the Breeze Markov Chain to generate a process of MetropState Calling .sample(n) on this will create a single site metropolis hastings, proposing parameters only from the initial supplied parameter values

    Definition Classes

    MetropolisHastings

16. def mhStep: (MetropState) ⇒ Rand[MetropState]

    

    A single step of the metropolis hastings algorithm to be used with breeze implementation of Markov Chain.

    A single step of the metropolis hastings algorithm to be used with breeze implementation of Markov Chain. This is a slight alteration to the implementation in breeze, here MetropState holds on to the previous calculated pseudo marginal log-likelihood value so we don't need to run the previous particle filter again each iteration

    Definition Classes

    MetropolisHastings

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. val prior: (Parameters) ⇒ LogLikelihood

    

    Prior distribution for the parameters, with default implementation

    Prior distribution for the parameters, with default implementation

    Definition Classes

    ParticleMetropolisHastings → MetropolisHastings

21. val proposal: (Parameters) ⇒ Rand[Parameters]

    

    a generic proposal distribution for the metropolis algorithm (eg.

    a generic proposal distribution for the metropolis algorithm (eg. Gaussian)

    Definition Classes

    ParticleMetropolisHastings → MetropolisHastings

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

    

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23. val transitionProb: (Parameters, Parameters) ⇒ LogLikelihood

    

24. final def wait(): Unit

    

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

    

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

    

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