Class/Object

io.github.mandar2812.dynaml.probability

ApproxBayesComputation

Related Docs: object ApproxBayesComputation | package probability

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class ApproxBayesComputation[ConditioningSet, Domain] extends RandomVariable[(ConditioningSet, Domain)] with BayesJointProbabilityScheme[ConditioningSet, Domain, RandomVariable[ConditioningSet], RandomVariable[Domain]]

Linear Supertypes
BayesJointProbabilityScheme[ConditioningSet, Domain, RandomVariable[ConditioningSet], RandomVariable[Domain]], JointProbabilityScheme[ConditioningSet, Domain, RandomVariable[ConditioningSet], RandomVariable[Domain]], RandomVariable[(ConditioningSet, Domain)], Serializable, Serializable, AnyRef, Any
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Inherited
  1. ApproxBayesComputation
  2. BayesJointProbabilityScheme
  3. JointProbabilityScheme
  4. RandomVariable
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new ApproxBayesComputation(p: RandomVariable[ConditioningSet], c: DataPipe[ConditioningSet, RandomVariable[Domain]], m: (Domain, Domain) ⇒ Double)

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

  3. def :*[Domain1](other: RandomVariable[Domain1]): RandomVariable[((ConditioningSet, Domain), Domain1)]

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    Outputs the cartesian product between two random variables.

    Outputs the cartesian product between two random variables.

    Domain1

    The domain of the other random variable

    other

    The random variable which forms the second component of the cartesian product.

    Definition Classes
    RandomVariable

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

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    Definition Classes
    AnyRef → Any

  5. def >[OtherDomain](transformation: DataPipe[(ConditioningSet, Domain), OtherDomain]): MeasurableFunction[(ConditioningSet, Domain), OtherDomain, RandomVariable[(ConditioningSet, Domain)]]

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    Transform the current random variable on Domain to a morphed random variable on OtherDomain

    Transform the current random variable on Domain to a morphed random variable on OtherDomain

    Definition Classes
    RandomVariable

  6. var MAX_ITERATIONS: Int

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    Attributes
    protected

  7. val acceptance: (Domain, Domain) ⇒ Boolean

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  8. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  9. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  10. def draw: (ConditioningSet, Domain)

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    Alias for sample.run()

    Alias for sample.run()

    Definition Classes
    RandomVariable

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

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    Definition Classes
    AnyRef

  12. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  13. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

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    Definition Classes
    AnyRef → Any

  15. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  16. def iid(n: Int): IIDRandomVariable[(ConditioningSet, Domain), RandomVariable[(ConditioningSet, Domain)]]

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    Create an iid random variable from the current (this)

    Create an iid random variable from the current (this)

    n

    The number of iid samples of the base random variable.

    Definition Classes
    RandomVariable

  17. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  18. val likelihood: DataPipe[ConditioningSet, RandomVariable[Domain]]

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    Definition Classes
    ApproxBayesComputation → JointProbabilityScheme

  19. def max_iterations_(it: Int): Unit

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  20. val metric: (Domain, Domain) ⇒ Double

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

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    Definition Classes
    AnyRef

  22. final def notify(): Unit

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    Definition Classes
    AnyRef

  23. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  24. val posterior: DataPipe[Domain, RandomVariable[ConditioningSet]]

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    Definition Classes
    ApproxBayesComputation → BayesJointProbabilityScheme

  25. val prior: RandomVariable[ConditioningSet]

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    Definition Classes
    ApproxBayesComputation → JointProbabilityScheme

  26. val sample: DataPipe[Unit, (ConditioningSet, Domain)]

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    Generate a sample from the random variable

    Generate a sample from the random variable

    Definition Classes
    JointProbabilityScheme → RandomVariable

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

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    Definition Classes
    AnyRef

  28. def toString(): String

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    Definition Classes
    AnyRef → Any

  29. var tolerance: Double

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    Attributes
    protected

  30. def tolerance_(t: Double): Unit

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  31. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from BayesJointProbabilityScheme[ConditioningSet, Domain, RandomVariable[ConditioningSet], RandomVariable[Domain]]

Inherited from JointProbabilityScheme[ConditioningSet, Domain, RandomVariable[ConditioningSet], RandomVariable[Domain]]

Inherited from RandomVariable[(ConditioningSet, Domain)]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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