java.lang.Object
- org.apache.commons.statistics.distribution.TruncatedNormalDistribution

All Implemented Interfaces:

ContinuousDistribution
```
public final class TruncatedNormalDistribution
extends Object
```
Implementation of the truncated normal distribution.
The probability density function of \( X \) is:
\[ f(x;\mu,\sigma,a,b) = \frac{1}{\sigma}\,\frac{\phi(\frac{x - \mu}{\sigma})}{\Phi(\frac{b - \mu}{\sigma}) - \Phi(\frac{a - \mu}{\sigma}) } \]
for \( \mu \) mean of the parent normal distribution, \( \sigma \) standard deviation of the parent normal distribution, \( -\infty \le a \lt b \le \infty \) the truncation interval, and \( x \in [a, b] \), where \( \phi \) is the probability density function of the standard normal distribution and \( \Phi \) is its cumulative distribution function.

See Also:

Truncated normal distribution (Wikipedia)

Nested Class Summary
- Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.ContinuousDistribution
  ContinuousDistribution.Sampler

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`ContinuousDistribution.Sampler`	`createSampler(org.apache.commons.rng.UniformRandomProvider rng)`	Creates a sampler.
`double`	`cumulativeProbability(double x)`	For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X <= x)`.
`double`	`density(double x)`	Returns the probability density function (PDF) of this distribution evaluated at the specified point `x`.
`double`	`getMean()`	Gets the mean of this distribution.
`double`	`getSupportLowerBound()`	Gets the lower bound of the support.
`double`	`getSupportUpperBound()`	Gets the upper bound of the support.
`double`	`getVariance()`	Gets the variance of this distribution.
`double`	`inverseCumulativeProbability(double p)`	Computes the quantile function of this distribution.
`double`	`inverseSurvivalProbability(double p)`	Computes the inverse survival probability function of this distribution.
`double`	`logDensity(double x)`	Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point `x`.
`static TruncatedNormalDistribution`	`of(double mean, double sd, double lower, double upper)`	Creates a truncated normal distribution.
`double`	`probability(double x0, double x1)`	For a random variable `X` whose values are distributed according to this distribution, this method returns `P(x0 < X <= x1)`.
`double`	`survivalProbability(double x)`	For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X > x)`.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - of
```
public static TruncatedNormalDistribution of(double mean,
                                             double sd,
                                             double lower,
                                             double upper)
```
    Creates a truncated normal distribution.
    Note that the mean and sd is of the parent normal distribution, and not the true mean and standard deviation of the truncated normal distribution. The lower and upper bounds define the truncation of the parent normal distribution.
    
    Parameters:
    
    mean - Mean for the parent distribution.
    
    sd - Standard deviation for the parent distribution.
    
    lower - Lower bound (inclusive) of the distribution, can be Double.NEGATIVE_INFINITY.
    
    upper - Upper bound (inclusive) of the distribution, can be Double.POSITIVE_INFINITY.
    
    Returns:
    
    the distribution
    
    Throws:
    
    IllegalArgumentException - if sd <= 0; if lower >= upper; or if the truncation covers no probability range in the parent distribution.
  - density
```
public double density(double x)
```
    Returns the probability density function (PDF) of this distribution evaluated at the specified point x. In general, the PDF is the derivative of the CDF. If the derivative does not exist at x, then an appropriate replacement should be returned, e.g. Double.POSITIVE_INFINITY, Double.NaN, or the limit inferior or limit superior of the difference quotient.
    
    Parameters:
    
    x - Point at which the PDF is evaluated.
    
    Returns:
    
    the value of the probability density function at x.
  - probability
```
public double probability(double x0,
                          double x1)
```
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1). The default implementation uses the identity P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
    
    Specified by:
    
    probability in interface ContinuousDistribution
    
    Parameters:
    
    x0 - Lower bound (exclusive).
    
    x1 - Upper bound (inclusive).
    
    Returns:
    
    the probability that a random variable with this distribution takes a value between x0 and x1, excluding the lower and including the upper endpoint.
  - logDensity
```
public double logDensity(double x)
```
    Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point x.
    
    Parameters:
    
    x - Point at which the PDF is evaluated.
    
    Returns:
    
    the logarithm of the value of the probability density function at x.
  - cumulativeProbability
```
public double cumulativeProbability(double x)
```
    For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x). In other words, this method represents the (cumulative) distribution function (CDF) for this distribution.
    
    Parameters:
    
    x - Point at which the CDF is evaluated.
    
    Returns:
    
    the probability that a random variable with this distribution takes a value less than or equal to x.
  - survivalProbability
```
public double survivalProbability(double x)
```
    For a random variable X whose values are distributed according to this distribution, this method returns P(X > x). In other words, this method represents the complementary cumulative distribution function.
    By default, this is defined as 1 - cumulativeProbability(x), but the specific implementation may be more accurate.
    
    Parameters:
    
    x - Point at which the survival function is evaluated.
    
    Returns:
    
    the probability that a random variable with this distribution takes a value greater than x.
  - inverseCumulativeProbability
```
public double inverseCumulativeProbability(double p)
```
    Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is:
    \[ x = \begin{cases} \inf \{ x \in \mathbb R : P(X \le x) \ge p\} & \text{for } 0 \lt p \le 1 \\ \inf \{ x \in \mathbb R : P(X \le x) \gt 0 \} & \text{for } p = 0 \end{cases} \]
    The default implementation returns:
    
    ContinuousDistribution.getSupportLowerBound() for p = 0,
    
    ContinuousDistribution.getSupportUpperBound() for p = 1, or
    
    the result of a search for a root between the lower and upper bound using cumulativeProbability(x) - p. The bounds may be bracketed for efficiency.
    Specified by:
    
    inverseCumulativeProbability in interface ContinuousDistribution
    
    Parameters:
    
    p - Cumulative probability.
    
    Returns:
    
    the smallest p-quantile of this distribution (largest 0-quantile for p = 0).
  - inverseSurvivalProbability
```
public double inverseSurvivalProbability(double p)
```
    Computes the inverse survival probability function of this distribution. For a random variable X distributed according to this distribution, the returned value is:
    \[ x = \begin{cases} \inf \{ x \in \mathbb R : P(X \gt x) \le p\} & \text{for } 0 \le p \lt 1 \\ \inf \{ x \in \mathbb R : P(X \gt x) \lt 1 \} & \text{for } p = 1 \end{cases} \]
    By default, this is defined as inverseCumulativeProbability(1 - p), but the specific implementation may be more accurate.
    The default implementation returns:
    
    ContinuousDistribution.getSupportLowerBound() for p = 1,
    
    ContinuousDistribution.getSupportUpperBound() for p = 0, or
    
    the result of a search for a root between the lower and upper bound using survivalProbability(x) - p. The bounds may be bracketed for efficiency.
    Specified by:
    
    inverseSurvivalProbability in interface ContinuousDistribution
    
    Parameters:
    
    p - Survival probability.
    
    Returns:
    
    the smallest (1-p)-quantile of this distribution (largest 0-quantile for p = 1).
  - createSampler
```
public ContinuousDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
```
    Creates a sampler.
    
    Specified by:
    
    createSampler in interface ContinuousDistribution
    
    Parameters:
    
    rng - Generator of uniformly distributed numbers.
    
    Returns:
    
    a sampler that produces random numbers according this distribution.
  - getMean
```
public double getMean()
```
    Gets the mean of this distribution.
    Represents the true mean of the truncated normal distribution rather than the parent normal distribution mean.
    For \( \mu \) mean of the parent normal distribution, \( \sigma \) standard deviation of the parent normal distribution, and \( a \lt b \) the truncation interval of the parent normal distribution, the mean is:
    \[ \mu + \frac{\phi(a)-\phi(b)}{\Phi(b) - \Phi(a)}\sigma \]
    where \( \phi \) is the probability density function of the standard normal distribution and \( \Phi \) is its cumulative distribution function.
    
    Returns:
    
    the mean.
  - getVariance
```
public double getVariance()
```
    Gets the variance of this distribution.
    Represents the true variance of the truncated normal distribution rather than the parent normal distribution variance.
    For \( \mu \) mean of the parent normal distribution, \( \sigma \) standard deviation of the parent normal distribution, and \( a \lt b \) the truncation interval of the parent normal distribution, the variance is:
    \[ \sigma^2 \left[1 + \frac{a\phi(a)-b\phi(b)}{\Phi(b) - \Phi(a)} - \left( \frac{\phi(a)-\phi(b)}{\Phi(b) - \Phi(a)} \right)^2 \right] \]
    where \( \phi \) is the probability density function of the standard normal distribution and \( \Phi \) is its cumulative distribution function.
    
    Returns:
    
    the variance.
  - getSupportLowerBound
```
public double getSupportLowerBound()
```
    Gets the lower bound of the support. It must return the same value as inverseCumulativeProbability(0), i.e. \( \inf \{ x \in \mathbb R : P(X \le x) \gt 0 \} \).
    The lower bound of the support is equal to the lower bound parameter of the distribution.
    
    Returns:
    
    the lower bound of the support.
  - getSupportUpperBound
```
public double getSupportUpperBound()
```
    Gets the upper bound of the support. It must return the same value as inverseCumulativeProbability(1), i.e. \( \inf \{ x \in \mathbb R : P(X \le x) = 1 \} \).
    The upper bound of the support is equal to the upper bound parameter of the distribution.
    
    Returns:
    
    the upper bound of the support.

Class TruncatedNormalDistribution

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.ContinuousDistribution

Method Summary

Methods inherited from class java.lang.Object

Method Detail

of

density

probability

logDensity

cumulativeProbability

survivalProbability

inverseCumulativeProbability

inverseSurvivalProbability

createSampler

getMean

getVariance

getSupportLowerBound

getSupportUpperBound