class MetropolisHastings[A] extends MarkovChain[A]
Metropolis-Hastings algorithm - generates random samples from a target distribution using only samples from a proposal distribution
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Instance Constructors
- new MetropolisHastings(generator: ProposalGenerator[A] with TransitionRatio[A], evaluator: DistributionEvaluator[A])(implicit random: Random)
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- protected
Value Members
- final def !=(arg0: Any): Boolean
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- final def ##(): Int
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- val evaluator: DistributionEvaluator[A]
- def finalize(): Unit
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- @throws(classOf[java.lang.Throwable])
- val generator: ProposalGenerator[A] with TransitionRatio[A]
- final def getClass(): Class[_ <: AnyRef]
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- def iterator(start: A, logger: AcceptRejectLogger[A]): Iterator[A]
start a logged iterator
- def iterator(current: A): Iterator[A]
provide a chain starting from current as an iterator
provide a chain starting from current as an iterator
- Definition Classes
- MarkovChain
- final def ne(arg0: AnyRef): Boolean
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- def next(current: A): A
next sample in chain
next sample in chain
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- MetropolisHastings → MarkovChain
- def next(current: A, logger: AcceptRejectLogger[A]): A
- final def notify(): Unit
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- final def notifyAll(): Unit
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- implicit val random: Random
- final def synchronized[T0](arg0: => T0): T0
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- def toString(): String
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- final def wait(): Unit
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