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java.lang.Objectumontreal.iro.lecuyer.probdist.ContinuousDistribution
umontreal.iro.lecuyer.probdist.UniformDist
public class UniformDist
Extends the class ContinuousDistribution
for
the uniform distribution
over the interval [a, b].
Its density is
Field Summary |
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Fields inherited from class umontreal.iro.lecuyer.probdist.ContinuousDistribution |
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decPrec |
Constructor Summary | |
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UniformDist()
Constructs a uniform distribution over the interval (a, b) = (0, 1). |
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UniformDist(double a,
double b)
Constructs a uniform distribution over the interval (a, b). |
Method Summary | |
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double |
barF(double x)
Returns bar(F)(x) = 1 - F(x). |
static double |
barF(double a,
double b,
double x)
Computes the uniform complementary distribution function bar(F)(x). |
double |
cdf(double x)
Computes and returns the distribution function F(x). |
static double |
cdf(double a,
double b,
double x)
Computes the uniform distribution function as in. |
double |
density(double x)
Returns f (x), the density of X evaluated at x. |
static double |
density(double a,
double b,
double x)
Computes the uniform density function f (x). |
double |
getA()
Returns the parameter a. |
double |
getB()
Returns the parameter b. |
static UniformDist |
getInstanceFromMLE(double[] x,
int n)
Creates a new instance of a uniform distribution with parameters a and b estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1. |
static double[] |
getMaximumLikelihoodEstimate(double[] x,
int n)
Estimates and returns the parameters [hat(a), hat(b)] of the uniform distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1. |
double |
getMean()
Returns the mean of the distribution function. |
static double |
getMean(double a,
double b)
Computes and returns the mean E[X] = (a + b)/2 of the uniform distribution with parameters a and b. |
double |
getStandardDeviation()
Returns the standard deviation of the distribution function. |
static double |
getStandardDeviation(double a,
double b)
Computes and returns the standard deviation of the uniform distribution with parameters a and b. |
double |
getVariance()
Returns the variance of the distribution function. |
static double |
getVariance(double a,
double b)
Computes and returns the variance Var[X] = (b - a)2/12 of the uniform distribution with parameters a and b. |
double |
inverseF(double u)
Computes and returns the inverse distribution function F-1(u), defined in. |
static double |
inverseF(double a,
double b,
double u)
Computes the inverse of the uniform distribution function. |
void |
setParams(double a,
double b)
Sets the parameters a and b for this object. |
Methods inherited from class umontreal.iro.lecuyer.probdist.ContinuousDistribution |
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inverseBisection, inverseBrent |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public UniformDist()
public UniformDist(double a, double b)
Method Detail |
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public double density(double x)
ContinuousDistribution
density
in class ContinuousDistribution
x
- value at which the density is evaluated
public double cdf(double x)
Distribution
x
- value at which the distribution function is evaluated
public double barF(double x)
Distribution
barF
in interface Distribution
barF
in class ContinuousDistribution
x
- value at which the complementary distribution function is evaluated
public double inverseF(double u)
Distribution
inverseF
in interface Distribution
inverseF
in class ContinuousDistribution
u
- value in the interval (0, 1) for which the inverse
distribution function is evaluated
public double getMean()
Distribution
public double getVariance()
Distribution
public double getStandardDeviation()
Distribution
public static double density(double a, double b, double x)
public static double cdf(double a, double b, double x)
public static double barF(double a, double b, double x)
public static double inverseF(double a, double b, double u)
public static UniformDist getInstanceFromMLE(double[] x, int n)
x
- the list of observations to use to evaluate parametersn
- the number of observations to use to evaluate parameterspublic static double[] getMaximumLikelihoodEstimate(double[] x, int n)
x
- the list of observations used to evaluate parametersn
- the number of observations used to evaluate parameters
public static double getMean(double a, double b)
public static double getVariance(double a, double b)
public static double getStandardDeviation(double a, double b)
public double getA()
public double getB()
public void setParams(double a, double b)
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SSJ V. 1.2.5. |
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