EMWithMH

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. def apply(emIterations: Int, mhParticles: Int, proposalScheme: ProposalScheme, p: Parameter[_]*)(implicit universe: Universe): GeneralizedEM

   An expectation maximization algorithm using Metropolis Hastings for inference.

   An expectation maximization algorithm using Metropolis Hastings for inference.

   emIterations

   number of iterations of the EM algorithm

   mhParticles

   number of particles of the MH algorithm

7. def apply(emIterations: Int, mhParticles: Int, p: Parameter[_]*)(implicit universe: Universe): GeneralizedEM

   An expectation maximization algorithm using Metropolis Hastings for inference.

   An expectation maximization algorithm using Metropolis Hastings for inference.

   emIterations

   number of iterations of the EM algorithm

   mhParticles

   number of particles of the MH algorithm

8. final def asInstanceOf[T0]: T0

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9. def clone(): AnyRef

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21. final def wait(): Unit

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

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