kmath-stat/space.kscience.kmath.samplers/AhrensDieterMarsagliaTsangGammaSampler

AhrensDieterMarsagliaTsangGammaSampler

class AhrensDieterMarsagliaTsangGammaSampler : Sampler<Double> (source)

Sampling from the gamma distribution.

For 0 < alpha < 1: Ahrens, J. H. and Dieter, U., Computer methods for sampling from gamma, beta, Poisson and binomial distributions, Computing, 12, 223-246, 1974.
For alpha >= 1: Marsaglia and Tsang, A Simple Method for Generating Gamma Variables. ACM Transactions on Mathematical Software, Volume 26 Issue 3, September, 2000.

Based on Commons RNG implementation.

See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.html.

Types

object Companion

Functions

suspend fun <T : Any> Sampler<T>.next(generator: RandomGenerator): T

Samples one value from this Sampler.

suspend fun Sampler<Double>.nextBuffer(generator: RandomGenerator, size: Int): Buffer<Double>

Samples a Buffer of values from this Sampler.

open override fun sample(generator: RandomGenerator): Chain<Double>

Generates a chain of samples.

@JvmName(name = "sampleRealBuffer")

fun Sampler<Double>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Double>>

Generates size real samples and chunks them into some buffers.

@JvmName(name = "sampleIntBuffer")

fun Sampler<Int>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Int>>

Generates size integer samples and chunks them into some buffers.

fun <T : Any> Sampler<T>.sampleBuffer(generator: RandomGenerator, size: Int, bufferFactory: BufferFactory<T> = BufferFactory.boxing()): Chain<Buffer<T>>

Sample a bunch of values

open override fun toString(): String