EMWithBP

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

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

   

   Definition Classes

   AnyRef → Any

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

   

   An expectation maximization algorithm using Belief Propagation for inference.

   An expectation maximization algorithm using Belief Propagation for inference.

   terminationCriteria

   criteria for stopping the EM algorithm

   bpIterations

   number of iterations of the BP algorithm

   params

   parameters to target with EM algorithm

5. def apply(emIterations: Int, bpIterations: Int, params: Parameter[_]*)(implicit universe: Universe): GeneralizedEM

   

   An expectation maximization algorithm using Belief Propagation for inference.

   An expectation maximization algorithm using Belief Propagation for inference.

   emIterations

   number of iterations of the EM algorithm

   bpIterations

   number of iterations of the BP algorithm

   params

   parameters to target with EM algorithm

6. def apply(params: Parameter[_]*)(implicit universe: Universe): GeneralizedEM

   

   An expectation maximization algorithm using Belief Propagation for inference.

   An expectation maximization algorithm using Belief Propagation for inference.

   params

   parameters to target with EM algorithm

7. def apply(emIterations: Int, bpIterations: Int, p: ModelParameters)(implicit universe: Universe): GeneralizedEM

   

   An expectation maximization algorithm using Belief Propagation for inference.

   An expectation maximization algorithm using Belief Propagation for inference.

   emIterations

   number of iterations of the EM algorithm

   bpIterations

   number of iterations of the BP algorithm

8. def apply(params: ModelParameters)(implicit universe: Universe): GeneralizedEM

   

   An expectation maximization algorithm using Belief Propagation sampling for inference.

   An expectation maximization algorithm using Belief Propagation sampling for inference.

   params

   parameters to target with EM algorithm

9. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

10. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

12. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

13. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

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

    

    Definition Classes

    AnyRef → Any

15. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

16. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

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

    

    Definition Classes

    AnyRef

18. final def notify(): Unit

    

    Definition Classes

    AnyRef

19. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

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

    

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

    

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

    

    Definition Classes

    AnyRef

23. def toString(): String

    

    Definition Classes

    AnyRef → Any

24. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )