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SMILE — Probability Distributions

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SMILE — Probability Distributions

The smile.stat.distribution package provides a comprehensive library of univariate and multivariate probability distributions, mixture models, and non-parametric density estimators. All distributions share a common interface, making it easy to swap them in algorithms.

Table of Contents

  1. Core Interfaces
  2. Continuous Univariate Distributions
  3. Discrete Univariate Distributions
  4. Multivariate Distributions
  5. Mixture Models
  6. Non-Parametric Density Estimation
  7. Parameter Estimation (MLE / MOM)
  8. Random Variate Generation
  9. Numerical Notes
  10. Quick Reference

1. Core Interfaces

Distribution

Every continuous univariate distribution implements this interface:

MethodDescription
p(double x)PDF value at x
logp(double x)Log-PDF (computed directly for stability)
cdf(double x)Cumulative distribution function P(X ≤ x)
quantile(double p)Inverse CDF (quantile function)
mean()Distribution mean
variance()Distribution variance
sd()Standard deviation (default: √variance())
entropy()Shannon entropy
rand()Draw one random sample
rand(int n)Draw n random samples
likelihood(double[] x)Likelihood of a data set
logLikelihood(double[] x)Log-likelihood of a data set

DiscreteDistribution

Discrete distributions additionally provide:

MethodDescription
p(int k)PMF value at integer k
logp(int k)Log-PMF
cdf(double k)CDF at k
randi()Draw one integer sample
randi(int n)Draw n integer samples

ExponentialFamily / DiscreteExponentialFamily

Distributions in the exponential family implement an M() method for the M-step of the EM algorithm, enabling them to be used inside mixture models.

2. Continuous Univariate Distributions

Gaussian (Normal) Distribution

GaussianDistribution(double mu, double sigma)

The bell-shaped distribution parameterised by mean μ and standard deviation σ.

java
// Create N(2.0, 1.5)
var g = new GaussianDistribution(2.0, 1.5);

System.out.println(g.p(2.0));      // ~0.2659 (mode value)
System.out.println(g.cdf(3.5));    // P(X ≤ 3.5)
System.out.println(g.quantile(0.975)); // 97.5th percentile

// Fit to data by MLE (estimates mu and sigma)
double[] data = g.rand(1000);
var est = GaussianDistribution.fit(data);
System.out.println(est); // GaussianDistribution(mu≈2.0, sigma≈1.5)

The shared standard-normal singleton is obtained via GaussianDistribution.getInstance(). It uses the Box-Muller algorithm for random variate generation. Its cache is automatically reset when MathEx.setSeed() is called, ensuring reproducible sequences.

Log-Normal Distribution

LogNormalDistribution(double mu, double sigma)

If ln(X) ~ N(μ, σ²) then X has a log-normal distribution.

java
var d = new LogNormalDistribution(1.0, 0.5);
System.out.println(d.mean());     // exp(mu + sigma²/2)
System.out.println(d.variance()); // (exp(sigma²)-1)*exp(2*mu+sigma²)

double[] data = d.rand(500);
var est = LogNormalDistribution.fit(data);

Note: variance() uses the correct formula (e^σ²-1)·e^(2μ+σ²). Older releases incorrectly used μ² instead of σ² in the first exponent.

Gamma Distribution

GammaDistribution(double k, double theta)

Shape parameter k > 0, scale parameter θ > 0. PDF: f(x) = x^(k−1) · e^(−x/θ) / (θ^k · Γ(k))

java
// Gamma(3, 2.1)  — shape=3, scale=2.1
var d = new GammaDistribution(3, 2.1);
System.out.println(d.mean());     // k * theta = 6.3
System.out.println(d.variance()); // k * theta² = 13.23

// Fit by MLE
double[] data = d.rand(1000);
var est = GammaDistribution.fit(data);

Fractional shape parameters are fully supported via the Marsaglia-Tsang (2000) algorithm. For 0 < k < 1, the implementation uses the Gamma(k+1) trick (X = Gamma(k+1) · U^(1/k)).

Special cases:

  • k=1, θ=1/λ → Exponential(λ)
  • k=ν/2, θ=2 → χ²(ν)

Exponential Distribution

ExponentialDistribution(double lambda)

Rate parameter λ > 0. Mean = 1/λ. Memoryless property.

java
var d = new ExponentialDistribution(2.0);  // rate=2, mean=0.5
System.out.println(d.mean());     // 0.5
System.out.println(d.cdf(1.0));   // 1 - e^{-2} ≈ 0.865

var est = ExponentialDistribution.fit(data);

Beta Distribution

BetaDistribution(double alpha, double beta)

Defined on [0, 1]; parameterised by shape parameters α > 0, β > 0.

java
var d = new BetaDistribution(2.0, 5.0);
System.out.println(d.mean());  // alpha/(alpha+beta) = 2/7
System.out.println(d.p(0.3));  // pdf at 0.3

var est = BetaDistribution.fit(data);

logp() is computed directly using lgamma for numerical precision, with correct edge-case handling at x=0 and x=1.

Weibull Distribution

WeibullDistribution(double k, double lambda)

Shape k > 0, scale λ > 0. Widely used in reliability / survival analysis.

java
var d = new WeibullDistribution(1.5, 2.0);
System.out.println(d.mean());
System.out.println(d.cdf(1.0));

// MLE fitting via Newton-Raphson
double[] data = d.rand(1000);
var est = WeibullDistribution.fit(data);
System.out.println("k=" + est.k + ", lambda=" + est.lambda);

Special case: k=1 → Exponential distribution.

Logistic Distribution

LogisticDistribution(double mu, double scale)

Location μ, positive scale s.

java
var d = new LogisticDistribution(0.0, 1.0);
System.out.println(d.p(0.0));   // 0.25
System.out.println(d.cdf(0.0)); // 0.5

logp() is computed directly using log1p(exp(-z)) for numerical stability in the tails.

t-Distribution (Student's t)

TDistribution(int nu)

Degrees of freedom ν ≥ 1. Used in hypothesis testing when population standard deviation is unknown.

java
var t20 = new TDistribution(20);
System.out.println(t20.cdf(2.086));  // one-sided p-value
System.out.println(t20.quantile(0.975)); // critical value

// Two-tailed tests
System.out.println(t20.cdf2tailed(2.086));
System.out.println(t20.quantile2tailed(0.05)); // two-tailed 5% critical value

Edge cases:

  • ν=1 (Cauchy): mean() and variance() throw UnsupportedOperationException
  • ν=2: variance() returns Double.POSITIVE_INFINITY

Random samples are generated via Z / √(χ²(ν)/ν) (ratio method), not the slow inverse-CDF bisection.

F-Distribution

FDistribution(int nu1, int nu2)

java
var f = new FDistribution(5, 20);
System.out.println(f.mean());      // nu2/(nu2-2)
System.out.println(f.cdf(3.0));
System.out.println(f.quantile(0.95)); // upper 5% critical value

p(x), logp(x), and cdf(x) return 0 / NEGATIVE_INFINITY for x ≤ 0 (no exception thrown). Random samples use the chi-square ratio method.

Chi-Square Distribution

ChiSquareDistribution(int nu)

Equivalent to Gamma(ν/2, 2). Used in goodness-of-fit and independence tests.

java
var chi = new ChiSquareDistribution(10);
System.out.println(chi.mean());       // nu
System.out.println(chi.quantile(0.95)); // upper 5% critical value

Random samples use the fast Marsaglia-Tsang GammaDistribution(ν/2, 2) path.

3. Discrete Univariate Distributions

Bernoulli Distribution

BernoulliDistribution(double p)

Single Bernoulli trial with success probability p ∈ [0, 1].

java
var d = new BernoulliDistribution(0.3);
System.out.println(d.p(0)); // q = 0.7
System.out.println(d.p(1)); // p = 0.3
System.out.println(d.mean());     // p
System.out.println(d.variance()); // p*(1-p)

// Fit from boolean array
boolean[] trials = {true, false, true, true, false};
var est = new BernoulliDistribution(trials);

logp(k) correctly handles the degenerate cases p=0 and p=1, returning 0.0 or NEGATIVE_INFINITY instead of NaN.

Binomial Distribution

BinomialDistribution(int n, double p)

Number of successes in n independent Bernoulli trials.

java
var d = new BinomialDistribution(100, 0.3);
System.out.println(d.p(30));       // P(X=30)
System.out.println(d.cdf(35));     // P(X≤35)
System.out.println(d.quantile(0.975));

// MLE
int[] data = d.randi(1000);
var est = BinomialDistribution.fit(data);

p(k) is computed as exp(logp(k)) to avoid the fragile floor(0.5 + exp(lchoose)) rounding used in older versions. logp() handles p=0 and p=1 without NaN.

Poisson Distribution

PoissonDistribution(double lambda)

Rate parameter λ ≥ 0.

java
var d = new PoissonDistribution(3.5);
System.out.println(d.p(3));    // P(X=3)
System.out.println(d.cdf(5));  // P(X≤5)

double[] data = /* counts */ null;
var est = PoissonDistribution.fit(/*int[] data*/);

Degenerate case λ=0: P(X=0)=1, all others 0. logp(0) returns 0.0 (not NaN from 0·log(0)).

Geometric Distribution

GeometricDistribution(double p)

Number of failures before the first success (0-indexed, support {0, 1, 2, …}).

java
var d = new GeometricDistribution(0.3);
System.out.println(d.p(0));   // 0.3  (immediate success)
System.out.println(d.mean()); // (1-p)/p

var est = GeometricDistribution.fit(data);

See also ShiftedGeometricDistribution (1-indexed: number of trials until first success, support {1, 2, 3, …}).

Negative Binomial Distribution

NegativeBinomialDistribution(double r, double p)

Number of successes before r failures; supports non-integer r.

java
var d = new NegativeBinomialDistribution(3.0, 0.3);
System.out.println(d.mean());      // r*(1-p)/p
System.out.println(d.variance());  // r*(1-p)/p²

// Method-of-moments fitting
int[] data = d.randi(2000);
var est = NegativeBinomialDistribution.fit(data);

Hypergeometric Distribution

HyperGeometricDistribution(int N, int m, int n)

Draws n items without replacement from a population of N containing m defects.

java
// N=50 items, 10 defective, draw 15
var d = new HyperGeometricDistribution(50, 10, 15);
System.out.println(d.mean());     // n*m/N = 3.0
System.out.println(d.p(3));
System.out.println(d.cdf(5));

Uses a fast patchwork-rejection algorithm for large n·m/(N) and an inversion method otherwise.

Empirical Distribution

EmpiricalDistribution(double[] prob)

A discrete distribution defined by an explicit probability table. Uses Walker's alias method for O(1) random sampling.

java
// Uniform over {0, 1, 2}
var d = new EmpiricalDistribution(new double[]{1.0/3, 1.0/3, 1.0/3});
System.out.println(d.rand()); // 0, 1, or 2

// Fit from integer data
int[] data = {0, 1, 1, 2, 0, 1};
var est = EmpiricalDistribution.fit(data);

// Fit from data with a custom value set (e.g. non-contiguous {0, 2, 5})
IntSet xs = IntSet.of(data);
var est2 = EmpiricalDistribution.fit(data, xs);

4. Multivariate Distributions

Multivariate Gaussian

MultivariateGaussianDistribution(double[] mean, DenseMatrix cov)
MultivariateGaussianDistribution(double[] mean, double[] variance) (diagonal)

java
double[] mu = {1.0, 0.0, -1.0};
double[][] sigma = {
    {0.9, 0.4, 0.7},
    {0.4, 0.5, 0.3},
    {0.7, 0.3, 0.8}
};

var d = new MultivariateGaussianDistribution(mu, DenseMatrix.of(sigma));
double[] x = {1.0, 0.0, -1.0};
System.out.println(d.p(x));    // pdf at x
System.out.println(d.cdf(x));  // cdf (Genz algorithm, error < 0.001)
double[] sample = d.rand();    // multivariate random sample

// MLE fitting
double[][] data = d.rand(500);
var est = MultivariateGaussianDistribution.fit(data);          // full cov
var estDiag = MultivariateGaussianDistribution.fit(data, true); // diagonal cov

CDF uses the Genz (1992) quasi-Monte Carlo algorithm with error < 0.001 in 99.9% of cases.

5. Mixture Models

Mixture models represent a distribution as a weighted sum of component distributions: P(x) = Σ wᵢ · P(x | component_i).

Finite Mixture (Continuous)

Mixture — base class for continuous mixtures.

java
// Manual construction
var c1 = new Mixture.Component(0.5, new GaussianDistribution(0.0, 1.0));
var c2 = new Mixture.Component(0.5, new GaussianDistribution(4.0, 1.0));
var mix = new Mixture(c1, c2);

System.out.println(mix.mean());     // 2.0  (weighted mean)
System.out.println(mix.variance()); // 5.0  (law of total variance)
System.out.println(mix.p(2.0));     // pdf at 2.0
System.out.println(mix.logp(2.0));  // log-pdf (log-sum-exp, numerically stable)

Important: variance() correctly applies the law of total variance: Var(X) = E[Var(X|Z)] + Var(E[X|Z]) = Σwᵢσᵢ² + Σwᵢμᵢ² − μ²

Gaussian Mixture Model (EM)

GaussianMixture.fit(double[] data) — automatically determines the number of components using BIC.

java
MathEx.setSeed(19650218);
double[] data = /* observed data */;
GaussianMixture mixture = GaussianMixture.fit(data);
System.out.println(mixture);           // e.g. "0.50 x N(0.0, 1.0) + 0.50 x N(4.0, 1.0)"
System.out.println(mixture.size());    // number of components
System.out.println(mixture.bic);       // BIC score

Exponential Family Mixture (EM)

Fit a mixture of any exponential-family distributions (Gaussian, Exponential, Gamma, etc.) using ExponentialFamilyMixture.fit():

java
MathEx.setSeed(19650218);
// Initial component configuration
var components = new Mixture.Component[]{
    new Mixture.Component(0.25, new GaussianDistribution(0.0, 1.0)),
    new Mixture.Component(0.25, new ExponentialDistribution(1.0)),
    new Mixture.Component(0.50, new GammaDistribution(1.0, 2.0))
};

ExponentialFamilyMixture result = ExponentialFamilyMixture.fit(data, components);
System.out.printf("Log-likelihood: %.4f%n", result.L);
System.out.printf("BIC: %.4f%n", result.bic);
for (var c : result.components) {
    System.out.printf("  %.2f x %s%n", c.priori(), c.distribution());
}

The EM algorithm uses the log-sum-exp trick in both the E-step and the log-likelihood evaluation to prevent numerical underflow.

Regularized EM

Pass a regularization parameter γ ∈ (0, 0.2] to encourage larger components to "absorb" probability mass from tiny components:

java
ExponentialFamilyMixture result =
    ExponentialFamilyMixture.fit(data, components, 0.1, 500, 1E-4);

Discrete Mixture (EM)

For integer-valued data, use DiscreteExponentialFamilyMixture:

java
var d1 = new DiscreteExponentialFamilyMixture.Component(0.5, new PoissonDistribution(2.0));
var d2 = new DiscreteExponentialFamilyMixture.Component(0.5, new PoissonDistribution(8.0));
var result = DiscreteExponentialFamilyMixture.fit(intData, d1, d2);

Multivariate Gaussian Mixture

java
MathEx.setSeed(42);
var gm = MultivariateGaussianMixture.fit(3, data); // 3 components
System.out.println(gm.size());   // number of fitted components
double[] x = data[0];
System.out.println(gm.p(x));     // pdf at x
int comp = gm.map(x);            // most probable component index

Posteriori Probability

All mixture classes expose posteriori(x) to compute the soft assignments:

java
GaussianMixture mix = GaussianMixture.fit(data);
double[] prob = mix.posteriori(2.5); // P(component_i | x=2.5)
int label = mix.map(2.5);            // hard assignment (most probable)

6. Non-Parametric Density Estimation

Kernel Density Estimation

KernelDensity(double[] data)
KernelDensity(double[] data, double bandwidth)

Uses a Gaussian kernel with Scott's rule for bandwidth selection (h = 1.06 · σ · n^(−1/5)).

java
double[] data = /* observed values */;
var kde = new KernelDensity(data);

System.out.println("bandwidth = " + kde.bandwidth()); // Scott's rule
System.out.println("mean  = " + kde.mean());
System.out.println("var   = " + kde.variance());

// Density evaluation
System.out.println(kde.p(3.5));     // pdf estimate
System.out.println(kde.logp(3.5));  // log-pdf (log-sum-exp)
System.out.println(kde.cdf(3.5));   // cdf estimate

// Inverse CDF (bisection on cdf)
System.out.println(kde.quantile(0.5)); // median

// Smoothed bootstrap sampling
double sample = kde.rand(); // data point + Gaussian noise

// Log-likelihood
System.out.println(kde.logLikelihood(testData));

The p() method restricts the kernel sum to the active window [x − 5h, x + 5h] using binary search for O(h/σ · n) evaluation.

7. Parameter Estimation (MLE / MOM)

All parametric distributions provide a static fit() method:

DistributionMethodLink
GaussianDistributionMLEGaussianDistribution.fit(double[])
LogNormalDistributionMLELogNormalDistribution.fit(double[])
GammaDistributionMLE (Newton-Raphson)GammaDistribution.fit(double[])
ExponentialDistributionMLEExponentialDistribution.fit(double[])
BetaDistributionMLE (Newton-Raphson)BetaDistribution.fit(double[])
WeibullDistributionMLE (Newton-Raphson)WeibullDistribution.fit(double[])
BinomialDistributionMLEBinomialDistribution.fit(int[])
PoissonDistributionMLEPoissonDistribution.fit(int[])
GeometricDistributionMLEGeometricDistribution.fit(int[])
ShiftedGeometricDistributionMLEShiftedGeometricDistribution.fit(int[])
NegativeBinomialDistributionMOMNegativeBinomialDistribution.fit(int[])
EmpiricalDistributionfrequency countEmpiricalDistribution.fit(int[])
MultivariateGaussianDistributionMLEMultivariateGaussianDistribution.fit(double[][])
GaussianMixtureEM (auto-select k)GaussianMixture.fit(double[])
ExponentialFamilyMixtureEMExponentialFamilyMixture.fit(double[], Component...)
MultivariateGaussianMixtureEMMultivariateGaussianMixture.fit(int k, double[][])

8. Random Variate Generation

SMILE uses the following algorithms for each distribution:

DistributionAlgorithm
GaussianDistributionBox-Muller (polar form)
GammaDistributionMarsaglia-Tsang (2000) for any shape k > 0
ChiSquareDistributionGammaDistribution(ν/2, 2)
TDistributionZ / √(χ²(ν)/ν)
FDistribution(χ²(d₁)/d₁) / (χ²(d₂)/d₂)
BetaDistributionInverse transform sampling
ExponentialDistribution-log(U)/λ (inverse transform)
LogNormalDistributionexp(GaussianDistribution.rand())
WeibullDistributionInverse transform: λ·(−log U)^(1/k)
BinomialDistributionChop-down / patchwork-rejection
PoissonDistributionInversion / patchwork-rejection
HyperGeometricDistributionPatchwork-rejection (HPRS) or inversion
GeometricDistributionExponential flooring
EmpiricalDistributionWalker's alias method O(1)

All generators respect the seed set via MathEx.setSeed(long seed). The Gaussian singleton's Box-Muller cache is automatically invalidated on re-seeding to ensure full reproducibility.

9. Numerical Notes

Log-Sum-Exp

logp(x) throughout the package is computed via direct formulas rather than log(p(x)). In mixture models, the log-density is evaluated via the numerically stable log-sum-exp identity:

log Σᵢ wᵢ·pᵢ(x)  =  max_i(log wᵢ + log pᵢ(x))
                     + log Σᵢ exp(log wᵢ + log pᵢ(x) − max)

Boundary Cases

Distributionx=0 behaviour
GammaDistribution(k>1, θ)p(0)=0, logp(0)=−∞
GammaDistribution(k=1, θ)p(0)=1/θ, logp(0)=−log θ
GammaDistribution(k<1, θ)p(0)=+∞, logp(0)=+∞
PoissonDistribution(λ=0)p(0)=1, logp(0)=0; no NaN
BinomialDistribution(n,p=0)p(0)=1, logp(0)=0; no NaN
FDistribution, BetaDistributionp(0)=0; no exception

Law of Total Variance

Mixture.variance() and MultivariateMixture.cov() implement the law of total variance / Bienaymé formula:

Var(X) = E[Var(X|Z)] + Var(E[X|Z])
       = Σᵢ wᵢ σᵢ²  +  Σᵢ wᵢ μᵢ²  −  μ²

10. Quick Reference

java
import smile.stat.distribution.*;

// --- Continuous ---
new GaussianDistribution(mu, sigma);
new LogNormalDistribution(mu, sigma);
new GammaDistribution(k, theta);
new ExponentialDistribution(lambda);
new BetaDistribution(alpha, beta);
new WeibullDistribution(k, lambda);
new LogisticDistribution(mu, scale);
new TDistribution(nu);
new FDistribution(nu1, nu2);
new ChiSquareDistribution(nu);

// --- Discrete ---
new BernoulliDistribution(p);
new BinomialDistribution(n, p);
new PoissonDistribution(lambda);
new GeometricDistribution(p);          // 0-indexed (failures before success)
new ShiftedGeometricDistribution(p);   // 1-indexed (trials until success)
new NegativeBinomialDistribution(r, p);
new HyperGeometricDistribution(N, m, n);
new EmpiricalDistribution(probArray);

// --- Multivariate ---
new MultivariateGaussianDistribution(mean, covMatrix);

// --- Mixture ---
GaussianMixture.fit(data);
ExponentialFamilyMixture.fit(data, components);
MultivariateGaussianMixture.fit(k, data);

// --- Non-parametric ---
new KernelDensity(data);
new KernelDensity(data, bandwidth);

// --- Common operations ---
dist.p(x);          // pdf / pmf
dist.logp(x);       // log pdf / pmf
dist.cdf(x);        // CDF
dist.quantile(p);   // inverse CDF
dist.rand();        // one sample
dist.rand(n);       // n samples
dist.mean();
dist.variance();
dist.entropy();
dist.logLikelihood(data);

SMILE — © 2010-2026 Haifeng Li. GNU GPL licensed.