HasConjugatePrior

abstract val conjugateFamily: ExponentialFamily[ConjugatePrior, Parameter]

abstract def distribution(p: Parameter): Likelihood

abstract def emptySufficientStatistic: SufficientStatistic

abstract def likelihoodFunction(stats: SufficientStatistic): DiffFunction[Parameter]

abstract def mle(stats: SufficientStatistic): Parameter

abstract def posterior(prior: Parameter, evidence: TraversableOnce[T]): Parameter

Gives a new parameter for this conjugate prior after observing the evidence.

abstract def predictive(parameter: Parameter): Density[T]

Returns a distribtution over T's after integrating out the intermediate distributions.

abstract def sufficientStatisticFor(t: T): SufficientStatistic

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit