Package smile.stat.distribution
Interface Distribution
- All Superinterfaces:
Serializable
- All Known Implementing Classes:
AbstractDistribution
,BernoulliDistribution
,BetaDistribution
,BinomialDistribution
,ChiSquareDistribution
,DiscreteDistribution
,DiscreteExponentialFamilyMixture
,DiscreteMixture
,EmpiricalDistribution
,ExponentialDistribution
,ExponentialFamilyMixture
,FDistribution
,GammaDistribution
,GaussianDistribution
,GaussianMixture
,GeometricDistribution
,HyperGeometricDistribution
,KernelDensity
,LogisticDistribution
,LogNormalDistribution
,Mixture
,NegativeBinomialDistribution
,PoissonDistribution
,ShiftedGeometricDistribution
,TDistribution
,WeibullDistribution
Probability distribution of univariate random variable. A probability
distribution identifies either the probability of each value
of a random variable (when the variable is discrete), or
the probability of the value falling within a particular interval (when
the variable is continuous). When the random variable takes values in the
set of real numbers, the probability distribution is completely described
by the cumulative distribution function, whose value at each real x is the
probability that the random variable is smaller than or equal to x.
- See Also:
-
Method Summary
Modifier and TypeMethodDescriptiondouble
cdf
(double x) Cumulative distribution function.double
entropy()
Shannon entropy of the distribution.int
length()
The number of parameters of the distribution.default double
likelihood
(double[] x) The likelihood of the sample set following this distribution.default double
logLikelihood
(double[] x) The log likelihood of the sample set following this distribution.double
logp
(double x) The density at x in log scale, which may prevents the underflow problem.double
mean()
The mean of distribution.double
p
(double x) The probability density function for continuous distribution or probability mass function for discrete distribution at x.double
quantile
(double p) The quantile, the probability to the left of quantile is p.double
rand()
Generates a random number following this distribution.default double[]
rand
(int n) Generates a set of random numbers following this distribution.default double
sd()
The standard deviation of distribution.double
variance()
The variance of distribution.
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Method Details
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length
int length()The number of parameters of the distribution. The "length" is in the sense of the minimum description length principle.- Returns:
- The number of parameters.
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mean
double mean()The mean of distribution.- Returns:
- The mean.
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variance
double variance()The variance of distribution.- Returns:
- The variance.
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sd
default double sd()The standard deviation of distribution.- Returns:
- The standard deviation.
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entropy
double entropy()Shannon entropy of the distribution.- Returns:
- Shannon entropy.
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rand
double rand()Generates a random number following this distribution.- Returns:
- a random number.
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rand
default double[] rand(int n) Generates a set of random numbers following this distribution.- Parameters:
n
- the number of random numbers to generate.- Returns:
- a set of random numbers.
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p
double p(double x) The probability density function for continuous distribution or probability mass function for discrete distribution at x.- Parameters:
x
- a real number.- Returns:
- the density.
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logp
double logp(double x) The density at x in log scale, which may prevents the underflow problem.- Parameters:
x
- a real number.- Returns:
- the log density.
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cdf
double cdf(double x) Cumulative distribution function. That is the probability to the left of x.- Parameters:
x
- a real number.- Returns:
- the probability.
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quantile
double quantile(double p) The quantile, the probability to the left of quantile is p. It is actually the inverse of cdf.- Parameters:
p
- the probability.- Returns:
- the quantile.
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likelihood
default double likelihood(double[] x) The likelihood of the sample set following this distribution.- Parameters:
x
- a set of samples.- Returns:
- the likelihood.
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logLikelihood
default double logLikelihood(double[] x) The log likelihood of the sample set following this distribution.- Parameters:
x
- a set of samples.- Returns:
- the log likelihood.
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