# Dirichlet

#### case class Dirichlet[T, I](params: T)(implicit space: TensorSpace[T, I, Double], rand: RandBasis = Rand, dav: DefaultArrayValue[T]) extends ContinuousDistr[T] with Product with Serializable

Represents a Dirichlet distribution, the conjugate prior to the multinomial.

Linear Supertypes
Serializable, Serializable, Product, Equals, ContinuousDistr[T], Rand[T], Density[T], AnyRef, Any
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1. Dirichlet
2. Serializable
3. Serializable
4. Product
5. Equals
6. ContinuousDistr
7. Rand
8. Density
9. AnyRef
10. Any
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### Value Members

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

Definition Classes
AnyRef
2. #### final def !=(arg0: Any): Boolean

Definition Classes
Any
3. #### final def ##(): Int

Definition Classes
AnyRef → Any
4. #### final def ==(arg0: AnyRef): Boolean

Definition Classes
AnyRef
5. #### final def ==(arg0: Any): Boolean

Definition Classes
Any
6. #### def apply(x: T): Double

Returns the unnormalized value of the measure

Returns the unnormalized value of the measure

Definition Classes
ContinuousDistrDensity
7. #### final def asInstanceOf[T0]: T0

Definition Classes
Any
8. #### def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
9. #### def condition(p: (T) ⇒ Boolean): Rand[T]

Definition Classes
Rand
10. #### def draw(): T

Returns a Multinomial distribution over the iterator

Returns a Multinomial distribution over the iterator

Definition Classes
DirichletRand
11. #### def drawOpt(): Option[T]

Overridden by filter/map/flatmap for monadic invocations.

Overridden by filter/map/flatmap for monadic invocations. Basically, rejeciton samplers will return None here

Definition Classes
Rand
12. #### final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
13. #### def filter(p: (T) ⇒ Boolean): Rand[T]

Definition Classes
Rand
14. #### def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
15. #### def flatMap[E](f: (T) ⇒ Rand[E]): Rand[E]

Converts a random sampler of one type to a random sampler of another type.

Converts a random sampler of one type to a random sampler of another type. Examples: randInt(10).flatMap(x => randInt(3 * x.asInstanceOf[Int]) gives a Rand[Int] in the range [0,30] Equivalently, for(x <- randInt(10); y <- randInt(30 *x)) yield y

f

the transform to apply to the sampled value.

Definition Classes
Rand
16. #### def foreach(f: (T) ⇒ Unit): Unit

Samples one element and qpplies the provided function to it.

Samples one element and qpplies the provided function to it. Despite the name, the function is applied once. Sample usage:

for(x <- Rand.uniform) { println(x) }

f

the function to be applied

Definition Classes
Rand
17. #### def get(): T

Definition Classes
Rand
18. #### final def getClass(): Class[_]

Definition Classes
AnyRef → Any
19. #### final def isInstanceOf[T0]: Boolean

Definition Classes
Any
20. #### def logApply(x: T): Double

Returns the log unnormalized value of the measure

Returns the log unnormalized value of the measure

Definition Classes
ContinuousDistrDensity
21. #### def logDraw(): T

Returns logNormalized probabilities.

Returns logNormalized probabilities. Use this if you're worried about underflow

22. #### val logNormalizer: Double

Definition Classes
DirichletContinuousDistr
23. #### def logPdf(x: T): Double

Definition Classes
ContinuousDistr
24. #### def map[E](f: (T) ⇒ E): Rand[E]

Converts a random sampler of one type to a random sampler of another type.

Converts a random sampler of one type to a random sampler of another type. Examples: uniform.map(_*2) gives a Rand[Double] in the range [0,2] Equivalently, for(x <- uniform) yield 2*x

f

the transform to apply to the sampled value.

Definition Classes
Rand
25. #### final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
26. #### final def notify(): Unit

Definition Classes
AnyRef
27. #### final def notifyAll(): Unit

Definition Classes
AnyRef

29. #### def pdf(x: T): Double

Returns the probability density function at that point.

Returns the probability density function at that point.

Definition Classes
ContinuousDistr
30. #### def sample(n: Int): IndexedSeq[T]

Gets n samples from the distribution.

Gets n samples from the distribution.

Definition Classes
Rand
31. #### def sample(): T

Gets one sample from the distribution.

Gets one sample from the distribution. Equivalent to get()

Definition Classes
Rand
32. #### def samples: Iterator[T]

An infinitely long iterator that samples repeatedly from the Rand

An infinitely long iterator that samples repeatedly from the Rand

returns

an iterator that repeatedly samples

Definition Classes
Rand
33. #### final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
34. #### def unnormalizedDraw(): T

Returns unnormalized probabilities for a Multinomial distribution.

35. #### def unnormalizedLogPdf(m: T): Double

Returns the log pdf function of the Dirichlet up to a constant evaluated at m

Returns the log pdf function of the Dirichlet up to a constant evaluated at m

Definition Classes
DirichletContinuousDistr
36. #### def unnormalizedPdf(x: T): Double

Returns the probability density function up to a constant at that point.

Returns the probability density function up to a constant at that point.

Definition Classes
ContinuousDistr
37. #### final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
38. #### final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
39. #### final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
40. #### def withFilter(p: (T) ⇒ Boolean): Rand[T]

Definition Classes
Rand