case class NegativeBinomial(r: Double, p: Double)(implicit rand: RandBasis) extends DiscreteDistr[Int] with Product with Serializable
Negative Binomial Distribution
- r
number of failures until stop
- p
prob of success
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- final def ##: Int
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- def apply(x: Int): Double
Returns the unnormalized value of the measure
Returns the unnormalized value of the measure
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- DiscreteDistr → Density
- final def asInstanceOf[T0]: T0
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- def condition(p: (Int) => Boolean): Rand[Int]
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- Rand
- def draw(): Int
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to sample()
- Definition Classes
- NegativeBinomial → Rand
- def drawOpt(): Option[Int]
Overridden by filter/map/flatmap for monadic invocations.
Overridden by filter/map/flatmap for monadic invocations. Basically, rejeciton samplers will return None here
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- Rand
- final def eq(arg0: AnyRef): Boolean
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- def filter(p: (Int) => Boolean): Rand[Int]
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- Rand
- def flatMap[E](f: (Int) => 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
- def foreach(f: (Int) => 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
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- Rand
- def get(): Int
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- Rand
- final def getClass(): Class[_ <: AnyRef]
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- def logApply(x: Int): Double
Returns the log unnormalized value of the measure
Returns the log unnormalized value of the measure
- Definition Classes
- DiscreteDistr → Density
- def logProbabilityOf(k: Int): Double
- Definition Classes
- NegativeBinomial → DiscreteDistr
- def map[E](f: (Int) => 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.
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- Rand
- final def ne(arg0: AnyRef): Boolean
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- val p: Double
- def probabilityOf(x: Int): Double
Returns the probability of that draw.
Returns the probability of that draw.
- Definition Classes
- NegativeBinomial → DiscreteDistr
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- val r: Double
- def sample(n: Int): IndexedSeq[Int]
Gets n samples from the distribution.
Gets n samples from the distribution.
- Definition Classes
- Rand
- def sample(): Int
Gets one sample from the distribution.
Gets one sample from the distribution. Equivalent to get()
- Definition Classes
- Rand
- def samples: Iterator[Int]
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
- def samplesVector[U >: Int](size: Int)(implicit m: ClassTag[U]): DenseVector[U]
Return a vector of samples.
Return a vector of samples.
- Definition Classes
- Rand
- final def synchronized[T0](arg0: => T0): T0
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- def unnormalizedLogProbabilityOf(x: Int): Double
- Definition Classes
- DiscreteDistr
- def unnormalizedProbabilityOf(x: Int): Double
Returns the probability of that draw up to a constant
Returns the probability of that draw up to a constant
- Definition Classes
- DiscreteDistr
- final def wait(arg0: Long, arg1: Int): Unit
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- final def wait(arg0: Long): Unit
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- final def wait(): Unit
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- def withFilter(p: (Int) => Boolean): Rand[Int]
- Definition Classes
- Rand