EMWithMH

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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. def apply(terminationCriteria: () ⇒ EMTerminationCriteria, mhParticles: Int, proposalScheme: ProposalScheme, params: ModelParameters)(implicit universe: Universe): GeneralizedEM

   

   An expectation maximization algorithm using Metropolis Hastings for inference.

   An expectation maximization algorithm using Metropolis Hastings for inference.

   terminationCriteria

   criteria for stopping the EM algorithm

   mhParticles

   number of particles of the MH algorithm

   proposalScheme

   proposal scheme for MH algorithm

   params

   parameters to target in EM algorithm

5. def apply(emIterations: Int, mhParticles: Int, proposalScheme: ProposalScheme, p: ModelParameters)(implicit universe: Universe): GeneralizedEM

   

   An expectation maximization algorithm using Metropolis Hastings for inference.

   An expectation maximization algorithm using Metropolis Hastings for inference.

   mhParticles

   number of particles of the MH algorithm

   proposalScheme

   proposal scheme for MH algorithm

6. def apply(p: ModelParameters)(implicit universe: Universe): GeneralizedEM

   

   An expectation maximization algorithm using Metropolis Hastings for inference.

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

   

   An expectation maximization algorithm using Metropolis Hastings for inference.

   An expectation maximization algorithm using Metropolis Hastings for inference.

   mhParticles

   number of particles of the MH algorithm

   proposalScheme

   proposal scheme for MH algorithm

   params

   parameters to target in EM algorithm

8. def apply(terminationCriteria: () ⇒ EMTerminationCriteria, mhParticles: Int, params: Parameter[_]*)(implicit universe: Universe): GeneralizedEM

   

   An expectation maximization algorithm using Metropolis Hastings for inference.

   An expectation maximization algorithm using Metropolis Hastings for inference.

   terminationCriteria

   criteria for stopping the EM algorithm

   mhParticles

   number of particles of the MH algorithm

   params

   parameters to target in EM algorithm

9. 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

10. final def asInstanceOf[T0]: T0

    

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11. def clone(): AnyRef

    

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

    

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13. def equals(arg0: Any): Boolean

    

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14. def finalize(): Unit

    

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

    

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16. def hashCode(): Int

    

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17. final def isInstanceOf[T0]: Boolean

    

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18. final def ne(arg0: AnyRef): Boolean

    

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19. final def notify(): Unit

    

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20. final def notifyAll(): Unit

    

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21. def online(transition: () ⇒ Universe, p: ModelParameters)(implicit universe: Universe): GeneralizedOnlineEM

    

22. def online(transition: () ⇒ Universe, p: Parameter[_]*)(implicit universe: Universe): GeneralizedOnlineEM

    

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

    

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24. def toString(): String

    

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25. final def wait(): Unit

    

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

    

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

    

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