Relationships among probability distributions

In probability theory and statistics, there are several relationships among probability distributions. These relations can be categorized in the following groups:

• One distribution is a special case of another with a broader parameter space
• Transforms (function of a random variable);
• Combinations (function of several variables);
• Approximation (limit) relationships;
• Compound relationships (useful for Bayesian inference);
• Duality;
• Conjugate priors.

Relationships among some of univariate probability distributions are illustrated with connected lines. dashed lines means approximate relationship. more info:^[1]

Special case of distribution parametrization

• A binomial (n, p) random variable with n = 1, is a Bernoulli (p) random variable.
• A negative binomial distribution with r = 1 is a geometric distribution.
• A gamma distribution with shape parameter α = 1 and scale parameter β is an exponential (β) distribution.
• A gamma (α, β) random variable with α = ν/2 and β = 2, is a chi-squared random variable with ν degrees of freedom.
• A chi-squared distribution with 2 degrees of freedom is an exponential distribution with mean 2 and vice versa.
• A Weibull (1, β) random variable is an exponential random variable with mean β.
• A beta random variable with parameters α = β = 1 is a uniform random variable.
• A beta-binomial (n, 1, 1) random variable is a discrete uniform random variable over the values 0 ... n.
• A random variable with a t distribution with one degree of freedom is a Cauchy(0,1) random variable.

Transform of a variable

Multiple of a random variable

Multiplying the variable by any positive real constant yields a scaling of the original distribution. Some are self-replicating, meaning that the scaling yields the same family of distributions, albeit with a different parameter: Normal distribution, Gamma distribution, Cauchy distribution, Exponential distribution, Erlang distribution, Weibull distribution, Logistic distribution, Error distribution, Power distribution, Rayleigh distribution.

Example:

• If X is a gamma random variable with parameters (r, λ), then Y=aX is a gamma random variable with parameters (r,λ/a).

Linear function of a random variable

The affine transform ax + b yields a relocation and scaling of the original distribution. The following are self-replicating: Normal distribution, Cauchy distribution, Logistic distribution, Error distribution, Power distribution, Rayleigh distribution.

Example:

• If Z is a normal random variable with parameters (μ=m, σ²=s²), then X=aZ+b is a normal random variable with parameters (μ=am+b, σ²=a²s²).

Reciprocal of a random variable

The reciprocal 1/X of a random variable X, is a member of the same family of distribution as X, in the following cases: Cauchy distribution, F distribution, log logistic distribution.

Examples:

• If X is a Cauchy (μ, σ) random variable, then 1/X is a Cauchy (μ/C, σ/C) random variable where C = μ² + σ².
• If X is an F(ν₁, ν₂) random variable then 1/X is an F(ν₂, ν₁) random variable.

Other cases

Some distributions are invariant under a specific transformation.

Example:

• If X is a beta (α, β) random variable then (1 - X) is a beta (β, α) random variable.
• If X is a binomial (n, p) random variable then (n - X) is a binomial (n, 1-p) random variable.
• If X has cumulative distribution function F_X, then F_X(X) is a standard uniform (0,1) random variable
• If X is a normal (μ, σ²) random variable then e^X is a lognormal (μ, σ²) random variable.

Conversely, if X is a lognormal (μ, σ²) random variable then log X is a normal (μ, σ²) random variable.

• If X is an exponential random variable with mean β, then X^1/γ is a Weibull (γ, β) random variable.
• The square of a standard normal random variable has a chi-squared distribution with one degree of freedom.
• If X is a Student’s t random variable with ν degree of freedom, then X² is an F (1,ν) random variable.
• If X is a double exponential random variable with mean 0 and scale λ, then |X| is an exponential random variable with mean λ.
• A geometric random variable is the floor of an exponential random variable.
• A rectangular random variable is the floor of a uniform random variable.
• A reciprocal random variable is the exponential of a uniform random variable.

Functions of several variables

Sum of variables

The distribution of the sum of independent random variables is called the convolution of the primal distribution.

• If it has a distribution from the same family of distributions as the original variables, that family of distributions is said to be closed under convolution.

Examples of such univariate distributions are: Normal distribution, Poisson distribution, Binomial distribution (with common success probability), Negative binomial distribution (with common success probability), Gamma distribution(with common rate parameter), Chi-squared distribution, Cauchy distribution, Hyper-exponential distribution.

Examples:^[2]

• • If X₁ and X₂ are Poisson random variables with means μ₁ and μ₂ respectively, then X₁ + X₂ is a Poisson random variable with mean μ₁ + μ₂.
  • The sum of gamma (n_i, β) random variables has a gamma (Σn_i, β) distribution.
  • If X₁ is a Cauchy (μ₁, σ₁) random variable and X₂ is a Cauchy (μ₂, σ₂), then X₁ + X₂ is a Cauchy (μ₁ + μ₂, σ₁ + σ₂) random variable.
  • If X₁ and X₂ are chi-squared random variables with ν₁ and ν₂ degrees of freedom respectively, then X₁ + X₂ is a chi-squared random variable with ν₁ + ν₂ degrees of freedom.
  • If X₁ is a normal (μ₁, σ₁²) random variable and X₂ is a normal (μ₂, σ₂²) random variable, then X₁ + X₂ is a normal (μ₁ + μ₂, σ₁² + σ₂²) random variable.
  • The sum of N chi-squared (1) random variables has a chi-squared distribution with N degrees of freedom.

Other distributions are not closed under convolution, but their sum has a known distribution:

• The sum of n Bernoulli (p) random variables is a binomial (n, p) random variable.
• The sum of n geometric random variable with probability of success p is a negative binomial random variable with parameters n and p.
• The sum of n exponential (β) random variables is a gamma (n, β) random variable.
• The sum of the squares of N standard normal random variables has a chi-squared distribution with N degrees of freedom.

Product of variables

The product of independent random variables X and Y may belong to the same family of distribution as X and Y: Bernoulli distribution and Log-normal distribution.

Example:

• If X₁ and X₂ are independent log-normal random variables with parameters (μ₁, σ₁²) and (μ₂, σ₂²) respectively, then X₁ X₂ is a log-normal random variable with parameters (μ₁ + μ₂, σ₁² + σ₂²).

Minimum and maximum of independent random variables

For some distributions, the minimum value of several independent random variables is a member of the same family, with different parameters: Bernoulli distribution, Geometric distribution, Exponential distribution, Extreme value distribution, Pareto distribution, Rayleigh distribution, Weibull distribution.

Examples:

• If X₁ and X₂ are independent geometric random variables with probability of success p₁ and p₂ respectively, then min(X₁, X₂) is a geometric random variable with probability of success p = p₁ + p₂ - p₁ p₂. The relationship is simpler if expressed in terms probability of failure: q = q₁ q₂.
• If X₁ and X₂ are independent exponential random variables with mean μ₁ and μ₂ respectively, then min(X₁, X₂) is an exponential random variable with mean μ₁ μ₂/(μ₁ + μ₂).

Similarly, distributions for which the maximum value of several independent random variables is a member of the same family of distribution include: Bernoulli distribution, Power distribution.

Other

• If X and Y are independent standard normal random variables, X/Y is a Cauchy (0,1) random variable.
• If X₁ and X₂ are chi-squared random variables with ν₁ and ν₂ degrees of freedom respectively, then (X₁/ν₁)/(X₂/ν₂) is an F(ν₁, ν₂) random variable.
• If X is a standard normal random variable and U is a chi-squared random variable with ν degrees of freedom, then $\frac{X}{\sqrt{(U/\nu)}}$ is a Student's t (ν) random variable.
• If X₁ is gamma (α₁, 1) random variable and X₂ is a gamma (α₂, 1) random variable then X₁/(X₁ + X₂) is a beta(α₁, α₂) random variable. More generally, if X₁is gamma(α₁, β₁) random variable and X₂ is gamma(α₂, β₂) random variable then β₂ X₁/(β₂ X₁ + β₁ X₂) is a beta(α₁, α₂) random variable.
• If X and Y are exponential random variables with mean μ, then X-Y is a double exponential random variable with mean 0 and scale μ.

Approximate (limit) relationships

Approximate or limit relationship means

• either that the combination of an infinite number of iid random variables tends to some distribution,
• or that the limit when a parameter tends to some value approaches to a different distribution.

Combination of iid random variables:

• Given certain conditions, the sum (hence the average) of a sufficiently large number of iid random variables, each with finite mean and variance, will be approximately normally distributed.(This is central limit theorem (CLT)).

Special case of distribution parametrization:

• X is a Hypergeometric (m, N, n) random variable. If n and m are large compared to N, and p = m / N is not close to 0 or 1, then X approximately has a Binomial(n, p) Distribution.
• X is a beta-binomial random variable with parameters (n, α, β). Let p = α/(α + β) and suppose α + β is large, then X approximately has a binomial(n, p) distribution.
• If X is a binomial (n, p) random variable and if n is large and np is small then X approximately has a Poisson(np) distribution.
• If X is a negative binomial random variable with r large, P near 1, and r(1-P) = λ, then X approximately has a Poisson distribution with mean λ.

Consequences of the CLT:

• If X is a Poisson random variable with large mean, then for integers j and k, P(j ≤ X ≤ k) approximately equals to P(j - 1/2 ≤ Y ≤ k + 1/2) where Y is a normal distribution with the same mean and variance as X.
• If X is a binomial(n, p) random variable with large n and np, then for integers j and k, P(j ≤ X ≤ k) approximately equals to P(j - 1/2 ≤ Y ≤ k + 1/2) where Y is a normal random variable with the same mean and variance as X, i. e. np and np(1-p).
• If X is a beta random variable with parameters α and β equal and large, then X approximately has a normal distribution with the same mean and variance, i. e. mean α/(α + β) and variance αβ/((α+β)²(α + β + 1)).
• If X is a gamma(α, β) random variable and the shape parameter α is large relative to the scale parameter β, then X approximately has a normal random variable with the same mean and variance.
• If X is a Student's t random variable with a large number of degrees of freedom ν then X approximately has a standard normal distribution.
• If X is an F(ν, ω) random variable with ω large, then ν X is approximately distributed As a chi-squared random variable with ν degrees of freedom.

Compound (or Bayesian) relationships

When one or more parameter(s) of a distribution are random variables, the compound distribution is the marginal distribution of the variable.

Examples:

• If X|N is a binomial (N,p) random variable, where parameter N is a random variable with negative-binomial (m, r) distribution, then X is distributed as a negative-binomial (m, r/(p+qr)).
• If X|N is a binomial (N,p) random variable, where parameter N is a random variable with Poisson (μ) distribution, then X is distributed as a Poisson (μp).
• If X|μ is a Poisson (μ) random variable and parameter μ is random variable with gamma (m, β) distribution, then X is distributed as a negative-binomial (m, μβ/(μ+β)), sometimes called Gamma-Poisson distribution if m is not integer.

Some distributions have been specially named as compounds: Beta-Binomial distribution, Beta-Pascal distribution, Gamma-Normal distribution.

Examples:

• If X is a Binomial (n,p) random variable, and parameter p is a random variable with beta (α, β) distribution, then X is distributed as a Beta-Binomial(α, β,n).
• If X is a negative-binomial (m,p) random variable, and parameter p is a random variable with beta (α, β) distribution, then X is distributed as a Beta-Pascal(α, β,m).

See also

• Central limit theorem
• Compound probability distribution

References

1. LEEMIS, Lawrence M.; Jacquelyn T. MCQUESTON (February 2008). "Univariate Distribution Relationships" (PDF). American Statistician 62 (1): 45–53. doi:10.1198/000313008x270448.
2. Cook, John D. "Diagram of distribution relationships".

External links

• Interactive graphic: Univariate Distribution Relationships