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SSJ
3.3.1
Stochastic Simulation in Java
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Extends the class ContinuousDistribution for the Laplace distribution (see, e.g., [100] (page 165)). More...
Public Member Functions | |
| LaplaceDist () | |
Constructs a LaplaceDist object with default parameters \(\mu= 0\) and \(\beta= 1\). | |
| LaplaceDist (double mu, double beta) | |
Constructs a LaplaceDist object with parameters \(\mu\) = mu and \(\beta\) = beta. | |
| double | density (double x) |
| double | cdf (double x) |
| Returns the distribution function \(F(x)\). More... | |
| double | barF (double x) |
| Returns \(\bar{F}(x) = 1 - F(x)\). More... | |
| double | inverseF (double u) |
| Returns the inverse distribution function \(F^{-1}(u)\), defined in ( inverseF ). More... | |
| double | getMean () |
| Returns the mean of the distribution function. | |
| double | getVariance () |
| Returns the variance of the distribution function. | |
| double | getStandardDeviation () |
| Returns the standard deviation of the distribution function. | |
| double | getMu () |
| Returns the parameter \(\mu\). | |
| double | getBeta () |
| Returns the parameter \(\beta\). | |
| double [] | getParams () |
| Return a table containing the parameters of the current distribution. More... | |
| String | toString () |
Returns a String containing information about the current distribution. | |
Public Member Functions inherited from ContinuousDistribution | |
| abstract double | density (double x) |
| Returns \(f(x)\), the density evaluated at \(x\). More... | |
| double | barF (double x) |
| Returns the complementary distribution function. More... | |
| double | inverseBrent (double a, double b, double u, double tol) |
| Computes the inverse distribution function \(x = F^{-1}(u)\), using the Brent-Dekker method. More... | |
| double | inverseBisection (double u) |
| Computes and returns the inverse distribution function \(x = F^{-1}(u)\), using bisection. More... | |
| double | inverseF (double u) |
| Returns the inverse distribution function \(x = F^{-1}(u)\). More... | |
| double | getMean () |
| Returns the mean. More... | |
| double | getVariance () |
| Returns the variance. More... | |
| double | getStandardDeviation () |
| Returns the standard deviation. More... | |
| double | getXinf () |
| Returns \(x_a\) such that the probability density is 0 everywhere outside the interval \([x_a, x_b]\). More... | |
| double | getXsup () |
| Returns \(x_b\) such that the probability density is 0 everywhere outside the interval \([x_a, x_b]\). More... | |
| void | setXinf (double xa) |
Sets the value \(x_a=\) xa, such that the probability density is 0 everywhere outside the interval \([x_a, x_b]\). More... | |
| void | setXsup (double xb) |
Sets the value \(x_b=\) xb, such that the probability density is 0 everywhere outside the interval \([x_a, x_b]\). More... | |
Static Public Member Functions | |
| static double | density (double mu, double beta, double x) |
| Computes the Laplace density function. | |
| static double | cdf (double mu, double beta, double x) |
| Computes the Laplace distribution function. | |
| static double | barF (double mu, double beta, double x) |
| Computes the Laplace complementary distribution function. | |
| static double | inverseF (double mu, double beta, double u) |
| Computes the inverse Laplace distribution function. | |
| static double [] | getMLE (double[] x, int n) |
| Estimates the parameters \((\mu, \beta)\) of the Laplace distribution using the maximum likelihood method, from the \(n\) observations \(x[i]\), \(i = 0, 1,…, n-1\). More... | |
| static LaplaceDist | getInstanceFromMLE (double[] x, int n) |
| Creates a new instance of a Laplace distribution with parameters \(\mu\) and \(\beta\) estimated using the maximum likelihood method based on the \(n\) observations \(x[i]\), \(i = 0, 1, …, n-1\). More... | |
| static double | getMean (double mu, double beta) |
| Computes and returns the mean \(E[X] = \mu\) of the Laplace distribution with parameters \(\mu\) and \(\beta\). More... | |
| static double | getVariance (double mu, double beta) |
| Computes and returns the variance \(\mbox{Var}[X] = 2 \beta^2\) of the Laplace distribution with parameters \(\mu\) and \(\beta\). More... | |
| static double | getStandardDeviation (double mu, double beta) |
| Computes and returns the standard deviation of the Laplace distribution with parameters \(\mu\) and \(\beta\). More... | |
Additional Inherited Members | |
Public Attributes inherited from ContinuousDistribution | |
| int | decPrec = 15 |
Protected Attributes inherited from ContinuousDistribution | |
| double | supportA = Double.NEGATIVE_INFINITY |
| double | supportB = Double.POSITIVE_INFINITY |
Static Protected Attributes inherited from ContinuousDistribution | |
| static final double | XBIG = 100.0 |
| static final double | XBIGM = 1000.0 |
| static final double [] | EPSARRAY |
Extends the class ContinuousDistribution for the Laplace distribution (see, e.g., [100] (page 165)).
It has location parameter \(\mu\) and scale parameter \(\beta> 0\). The density function is given by
\[ f(x) = \frac{e^{-|x - \mu|/\beta}}{2\beta} \qquad\mbox{ for }-\infty< x < \infty. \tag{flaplace} \]
The distribution function is
\[ F (x) = \left\{\begin{array}{ll} \frac{1}{2} e^{(x - \mu)/\beta} & \mbox{ if } x\le\mu, \\ 1 - \frac{1}{2} e^{(\mu- x)/\beta} & \mbox{ otherwise, } \end{array}\right. \]
and its inverse is
\[ F^{-1} (u) = \left\{\begin{array}{ll} \beta\log(2u) + \mu & \mbox{ if } 0\le u\le\frac{1}{2}, \\ \mu- \beta\log(2(1-u)) & \mbox{ otherwise. } \end{array}\right. \]
| double barF | ( | double | x | ) |
Returns \(\bar{F}(x) = 1 - F(x)\).
| x | value at which the complementary distribution function is evaluated |
x Implements Distribution.
| double cdf | ( | double | x | ) |
Returns the distribution function \(F(x)\).
| x | value at which the distribution function is evaluated |
x Implements Distribution.
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Creates a new instance of a Laplace distribution with parameters \(\mu\) and \(\beta\) estimated using the maximum likelihood method based on the \(n\) observations \(x[i]\), \(i = 0, 1, …, n-1\).
| x | the list of observations to use to evaluate parameters |
| n | the number of observations to use to evaluate parameters |
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Computes and returns the mean \(E[X] = \mu\) of the Laplace distribution with parameters \(\mu\) and \(\beta\).
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Estimates the parameters \((\mu, \beta)\) of the Laplace distribution using the maximum likelihood method, from the \(n\) observations \(x[i]\), \(i = 0, 1,…, n-1\).
The estimates are returned in a two-element array, in regular order: [ \(\mu\), \(\beta\)]. The maximum likelihood estimators are the values \((\hat{\mu}, \hat{\beta})\) that satisfy the equations:
\begin{align*} \hat{\mu} & = \mbox{the median of } \{x_1,…,x_n\} \\ \hat{\beta} & = \frac{1}{n} \sum_{i=1}^n |x_i - \hat{\mu}|. \end{align*}
See [100] (page 172).
| x | the list of observations used to evaluate parameters |
| n | the number of observations used to evaluate parameters |
| double [] getParams | ( | ) |
Return a table containing the parameters of the current distribution.
This table is put in regular order: [ \(\mu\), \(\beta\)].
Implements Distribution.
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Computes and returns the standard deviation of the Laplace distribution with parameters \(\mu\) and \(\beta\).
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Computes and returns the variance \(\mbox{Var}[X] = 2 \beta^2\) of the Laplace distribution with parameters \(\mu\) and \(\beta\).
| double inverseF | ( | double | u | ) |
Returns the inverse distribution function \(F^{-1}(u)\), defined in ( inverseF ).
| u | value in the interval \((0,1)\) for which the inverse distribution function is evaluated |
u Implements Distribution.
1.8.14