Package dev.nm.stat.random.rng.univariate.normal.truncated

Class InverseTransformSamplingTruncatedNormalRNG

java.lang.Object

dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG

All Implemented Interfaces:

RandomGammaGenerator, RandomNumberGenerator, Seedable

public class InverseTransformSamplingTruncatedNormalRNG
extends Object
implements RandomGammaGenerator

A random variate x defined as \[ x = \Phi^{-1}( \Phi(\alpha) + U\cdot(\Phi(\beta)-\Phi(\alpha)))\sigma + \mu \] with \(\Phi\) the cumulative distribution function and \(\Phi^{-1}\) its inverse, U a uniform random number on (0, 1), follows the distribution truncated to the range (a, b). This method is theoretically the best, however the simulation of random variables from \(\Phi\) and \(\Phi^{-1}\) may imply numerical errors; thus practically one has to find other implementations.

See Also:

• Wikipedia: Simulating

Constructor Summary

Constructors

Constructor	Description
InverseTransformSamplingTruncatedNormalRNG(double a, double b)	Construct a rng that samples from a truncated standard Normal distribution using inverse sampling technique.
InverseTransformSamplingTruncatedNormalRNG(double mu, double sigma, double a, double b)	Construct a rng that samples from a truncated Normal distribution using inverse sampling technique.
InverseTransformSamplingTruncatedNormalRNG(double mu, double sigma, double a, double b, RandomLongGenerator uniform)	Construct a rng that samples from a truncated Normal distribution using inverse sampling technique.

Method Summary

Modifier and Type	Method	Description
double	nextDouble()	Get the next random double.
void	seed(long... seeds)	Seed the random number/vector/scenario generator to produce repeatable experiments.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

InverseTransformSamplingTruncatedNormalRNG

public InverseTransformSamplingTruncatedNormalRNG(double mu,
 double sigma,
 double a,
 double b,
 RandomLongGenerator uniform)

Construct a rng that samples from a truncated Normal distribution using inverse sampling technique.

Parameters:

mu - the mean

sigma - the standard deviation

a - the lower bound

b - the upper bound

uniform - a uniform rlg

InverseTransformSamplingTruncatedNormalRNG

public InverseTransformSamplingTruncatedNormalRNG(double mu,
 double sigma,
 double a,
 double b)

Construct a rng that samples from a truncated Normal distribution using inverse sampling technique.

Parameters:

mu - the mean

sigma - the standard deviation

a - the lower bound

b - the upper bound

InverseTransformSamplingTruncatedNormalRNG

public InverseTransformSamplingTruncatedNormalRNG(double a,
 double b)

Construct a rng that samples from a truncated standard Normal distribution using inverse sampling technique.

Parameters:

a - the lower bound

b - the upper bound

Method Details

nextDouble

public double nextDouble()

Description copied from interface: RandomNumberGenerator

Get the next random double.

Specified by:

nextDouble in interface RandomNumberGenerator

Returns:

the next random number

seed

public void seed(long... seeds)

Description copied from interface: Seedable

Seed the random number/vector/scenario generator to produce repeatable experiments.

Specified by:

seed in interface Seedable

Parameters:

seeds - the seeds