public class BinomialDistributionImpl extends AbstractIntegerDistribution implements BinomialDistribution, Serializable
BinomialDistribution
.randomData
Constructor and Description |
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BinomialDistributionImpl(int trials,
double p)
Create a binomial distribution with the given number of trials and
probability of success.
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Modifier and Type | Method and Description |
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double |
cumulativeProbability(int x)
For this distribution, X, this method returns P(X ≤ x).
|
protected int |
getDomainLowerBound(double p)
Access the domain value lower bound, based on
p , used to
bracket a PDF root. |
protected int |
getDomainUpperBound(double p)
Access the domain value upper bound, based on
p , used to
bracket a PDF root. |
int |
getNumberOfTrials()
Access the number of trials for this distribution.
|
double |
getNumericalMean()
Returns the mean.
|
double |
getNumericalVariance()
Returns the variance.
|
double |
getProbabilityOfSuccess()
Access the probability of success for this distribution.
|
int |
getSupportLowerBound()
Returns the lower bound of the support for the distribution.
|
int |
getSupportUpperBound()
Returns the upper bound of the support for the distribution.
|
int |
inverseCumulativeProbability(double p)
For this distribution, X, this method returns the largest x, such that
P(X ≤ x) ≤
p . |
double |
probability(int x)
For this distribution, X, this method returns P(X = x).
|
void |
setNumberOfTrials(int trials)
Deprecated.
as of 2.1 (class will become immutable in 3.0)
|
void |
setProbabilityOfSuccess(double p)
Deprecated.
as of 2.1 (class will become immutable in 3.0)
|
cumulativeProbability, cumulativeProbability, cumulativeProbability, isSupportLowerBoundInclusive, isSupportUpperBoundInclusive, probability, reseedRandomGenerator, sample, sample
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
cumulativeProbability
probability
cumulativeProbability, cumulativeProbability
public BinomialDistributionImpl(int trials, double p)
trials
- the number of trials.p
- the probability of success.public int getNumberOfTrials()
getNumberOfTrials
in interface BinomialDistribution
public double getProbabilityOfSuccess()
getProbabilityOfSuccess
in interface BinomialDistribution
@Deprecated public void setNumberOfTrials(int trials)
setNumberOfTrials
in interface BinomialDistribution
trials
- the new number of trials.IllegalArgumentException
- if trials
is not a valid
number of trials.@Deprecated public void setProbabilityOfSuccess(double p)
setProbabilityOfSuccess
in interface BinomialDistribution
p
- the new probability of success.IllegalArgumentException
- if p
is not a valid
probability.protected int getDomainLowerBound(double p)
p
, used to
bracket a PDF root.getDomainLowerBound
in class AbstractIntegerDistribution
p
- the desired probability for the critical valuep
protected int getDomainUpperBound(double p)
p
, used to
bracket a PDF root.getDomainUpperBound
in class AbstractIntegerDistribution
p
- the desired probability for the critical valuep
public double cumulativeProbability(int x) throws MathException
cumulativeProbability
in interface IntegerDistribution
cumulativeProbability
in class AbstractIntegerDistribution
x
- the value at which the PDF is evaluated.MathException
- if the cumulative probability can not be computed
due to convergence or other numerical errors.public double probability(int x)
probability
in interface IntegerDistribution
x
- the value at which the PMF is evaluated.public int inverseCumulativeProbability(double p) throws MathException
p
.
Returns -1
for p=0 and Integer.MAX_VALUE
for
p=1.
inverseCumulativeProbability
in interface IntegerDistribution
inverseCumulativeProbability
in class AbstractIntegerDistribution
p
- the desired probabilityMathException
- if the inverse cumulative probability can not be
computed due to convergence or other numerical errors.IllegalArgumentException
- if p < 0 or p > 1public int getSupportLowerBound()
public int getSupportUpperBound()
public double getNumericalMean()
n
number of trials and
probability parameter p
, the mean is
n * p
public double getNumericalVariance()
n
number of trials and
probability parameter p
, the variance is
n * p * (1 - p)
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