Famille exponentielle

In probability and statistics, an exponential family is a parametric set of probability distributions of a certain form, specified below. This special form is chosen for mathematical convenience, based on some useful algebraic properties, as well as for generality, as exponential families are in a sense very natural sets of distributions to consider. The term exponential class is sometimes used in place of "exponential family",[1] or the older term Koopman–Darmois family. Les termes "distribution" et "family" are often used loosely: spécifiquement, an exponential family is a set of distributions, where the specific distribution varies with the parameter;[un] toutefois, a parametric family of distributions is often referred to as "a distribution" (like "the normal distribution", meaning "the family of normal distributions"), and the set of all exponential families is sometimes loosely referred to as "la" exponential family. They are distinct because they possess a variety of desirable properties, most importantly the existence of a sufficient statistic.

The concept of exponential families is credited to[2] E. J. g. Pitman,[3] g. Darmois,[4] et B. O. Koopman[5] in 1935–1936. Exponential families of distributions provides a general framework for selecting a possible alternative parameterisation of a parametric family of distributions, in terms of natural parameters, and for defining useful sample statistics, called the natural sufficient statistics of the family.

Contenu 1 Définition 1.1 Examples of exponential family distributions 1.2 Scalar parameter 1.3 Factorization of the variables involved 1.4 Vector parameter 1.5 Vector parameter, vector variable 1.6 Measure-theoretic formulation 2 Interpretation 3 Propriétés 4 Exemples 4.1 Normal distribution: unknown mean, known variance 4.2 Normal distribution: unknown mean and unknown variance 4.3 Binomial distribution 5 Table of distributions 6 Moments and cumulants of the sufficient statistic 6.1 Normalization of the distribution 6.2 Moment-generating function of the sufficient statistic 6.2.1 Differential identities for cumulants 6.2.2 Exemple 1 6.2.3 Exemple 2 6.2.4 Exemple 3 7 Entropy 7.1 Relative entropy 7.2 Maximum-entropy derivation 8 Role in statistics 8.1 Classical estimation: sufficiency 8.2 Bayesian estimation: conjugate distributions 8.3 Hypothesis testing: uniformly most powerful tests 8.4 Generalized linear models 9 Voir également 10 Notes de bas de page 11 Références 11.1 Citations 11.2 Sources 12 Lectures complémentaires 13 External links Definition Most of the commonly used distributions form an exponential family or subset of an exponential family, listed in the subsection below. The subsections following it are a sequence of increasingly more general mathematical definitions of an exponential family. A casual reader may wish to restrict attention to the first and simplest definition, which corresponds to a single-parameter family of discrete or continuous probability distributions.

Examples of exponential family distributions Exponential families include many of the most common distributions. Among many others, exponential families includes the following: normal exponential gamma chi-squared beta Dirichlet Bernoulli categorical Poisson Wishart inverse Wishart geometric A number of common distributions are exponential families, but only when certain parameters are fixed and known. Par exemple: binomial (with fixed number of trials) multinomial (with fixed number of trials) negative binomial (with fixed number of failures) Notice that in each case, the parameters which must be fixed determine a limit on the size of observation values.

Examples of common distributions that are not exponential families are Student's t, most mixture distributions, and even the family of uniform distributions when the bounds are not fixed. See the section below on examples for more discussion.

Scalar parameter A single-parameter exponential family is a set of probability distributions whose probability density function (or probability mass function, for the case of a discrete distribution) can be expressed in the form {je}2(xmid theta )=h(X),exp !{je}1,eta (thêta )cdot T(X)-UN(thêta ),{je}0} where T(X), h(X), η(je), et un(je) are known functions. The function h(X) must of course be non-negative.

An alternative, equivalent form often given is {symbole gras {chi }9(xmid theta )=h(X),g(thêta ),exp !{symbole gras {chi }8,eta (thêta )cdot T(X),{symbole gras {chi }7} ou équivalent {symbole gras {chi }6(xmid theta )= exp !{symbole gras {chi }5,eta (thêta )cdot T(X)-UN(thêta )+B(X),{symbole gras {chi }4} The value θ is called the parameter of the family.

en outre, the support of {symbole gras {chi }3!la gauche(xmid theta right)} (c'est à dire. the set of all {symbole gras {chi }2 Pour qui {symbole gras {chi }1!la gauche(xmid theta right)} is greater than 0) does not depend on {symbole gras {chi }0 .[6] This can be used to exclude a parametric family distribution from being an exponential family. Par exemple, the Pareto distribution has a pdf which is defined for {symbole gras {eta }9} ( {symbole gras {eta }8} being the scale parameter) and its support, Donc, has a lower limit of {symbole gras {eta }7} . Since the support of {symbole gras {eta }6}!(X)} is dependent on the value of the parameter, the family of Pareto distributions does not form an exponential family of distributions (at least when {symbole gras {eta }5} is unknown).

Often x is a vector of measurements, in which case T(X) may be a function from the space of possible values of x to the real numbers. Plus généralement, η(je) and T(X) can each be vector-valued such that {symbole gras {eta }4 is real-valued. Cependant, see the discussion below on vector parameters, regarding the curved exponential family.

If η(je) = θ, then the exponential family is said to be in canonical form. By defining a transformed parameter η = η(je), it is always possible to convert an exponential family to canonical form. The canonical form is non-unique, since η(je) can be multiplied by any nonzero constant, provided that T(X) is multiplied by that constant's reciprocal, or a constant c can be added to η(je) et h(X) multiplié par {symbole gras {eta }3-ccdot T(X),{symbole gras {eta }2} to offset it. In the special case that η(je) = θ and T(X) = x then the family is called a natural exponential family.

Even when x is a scalar, and there is only a single parameter, the functions η(je) and T(X) can still be vectors, as described below.

The function A(je), or equivalently g(je), is automatically determined once the other functions have been chosen, since it must assume a form that causes the distribution to be normalized (sum or integrate to one over the entire domain). Par ailleurs, both of these functions can always be written as functions of η, even when η(je) is not a one-to-one function, c'est à dire. two or more different values of θ map to the same value of η(je), and hence η(je) cannot be inverted. Dans ce cas, all values of θ mapping to the same η(je) will also have the same value for A(je) et g(je).

Factorization of the variables involved What is important to note, and what characterizes all exponential family variants, is that the parameter(s) and the observation variable(s) must factorize (can be separated into products each of which involves only one type of variable), either directly or within either part (the base or exponent) of an exponentiation operation. Généralement, this means that all of the factors constituting the density or mass function must be of one of the following forms: {symbole gras {eta }1,c^{symbole gras {eta }0,{[F(X)]}^{nu }9,{[g(thêta )]}^{nu }8,{[F(X)]}^{nu }7,{[g(thêta )]}^{nu }6,{[F(X)]}^{nu }5,{nu }4}{[g(thêta )]}^{nu }3,} where f and h are arbitrary functions of x; g and j are arbitrary functions of θ; and c is an arbitrary "constant" expression (c'est à dire. an expression not involving x or θ).

There are further restrictions on how many such factors can occur. Par exemple, the two expressions: {nu }2^{nu }1,qquad {[F(X)]}^{nu }0[g(thêta )]^{symbole gras {eta }9,} sont identiques, c'est à dire. a product of two "allowed" factors. Cependant, when rewritten into the factorized form, {symbole gras {eta }8^{symbole gras {eta }7={[F(X)]}^{symbole gras {eta }6[g(thêta )]^{symbole gras {eta }5=e^{[h(X)log f(X)]j(thêta )+h(X)[j(thêta )log g(thêta )]},} it can be seen that it cannot be expressed in the required form. (Cependant, a form of this sort is a member of a curved exponential family, which allows multiple factorized terms in the exponent.[citation requise]) To see why an expression of the form {symbole gras {eta }4^{symbole gras {eta }3} qualifies, {symbole gras {eta }2^{symbole gras {eta }1=e^{symbole gras {eta }0} and hence factorizes inside of the exponent. De la même manière, {rm {J}9^{rm {J}8=e^{rm {J}7=e^{[h(X)log f(X)]g(thêta )}} and again factorizes inside of the exponent.

A factor consisting of a sum where both types of variables are involved (par exemple. a factor of the form {rm {J}6 ) cannot be factorized in this fashion (except in some cases where occurring directly in an exponent); this is why, par exemple, the Cauchy distribution and Student's t distribution are not exponential families.

Vector parameter The definition in terms of one real-number parameter can be extended to one real-vector parameter {rm {J}5}equiv left[,thêta _{rm {J}4,,thêta _{rm {J}3,,ldots ,,thêta _{rm {J}2,droit]^{rm {J}1}~.} A family of distributions is said to belong to a vector exponential family if the probability density function (or probability mass function, for discrete distributions) can be written as {rm {J}0(xmid {symbole gras {chi }9})=h(X),exp gauche(somme _{symbole gras {chi }8^{symbole gras {chi }7eta _{symbole gras {chi }6({symbole gras {chi }5})T_{symbole gras {chi }4(X)-UN({symbole gras {chi }3})droit)~,} or in a more compact form, {symbole gras {chi }2(xmid {symbole gras {chi }1})=h(X),exp {symbole gras {chi }0{symbole gras {eta }9}({symbole gras {eta }8})cdot mathbf {symbole gras {eta }7 (X)-UN({symbole gras {eta }6}){symbole gras {eta }5} This form writes the sum as a dot product of vector-valued functions {symbole gras {eta }4}({symbole gras {eta }3})} et {symbole gras {eta }2 (X),} .

An alternative, equivalent form often seen is {symbole gras {eta }1(xmid {symbole gras {eta }0})=h(X),g({n}9}),exp {n}8{n}7}({n}6})cdot mathbf {n}5 (X){n}4} As in the scalar valued case, the exponential family is said to be in canonical form if {n}3({n}2})=thêta _{n}1quad forall i,.} A vector exponential family is said to be curved if the dimension of {n}0}equiv left[,thêta _{symbole gras {eta }9,,thêta _{symbole gras {eta }8,,ldots ,,thêta _{symbole gras {eta }7,,droit]^{symbole gras {eta }6}} is less than the dimension of the vector {symbole gras {eta }5}({symbole gras {eta }4})equiv left[,eta _{symbole gras {eta }3({symbole gras {eta }2}),,eta _{symbole gras {eta }1({symbole gras {eta }0}),,ldots ,,eta _{rm {J}9({rm {J}8}),droit]^{rm {J}7}~.} C'est-à-dire, if the dimension, ré, of the parameter vector is less than the number of functions, s, of the parameter vector in the above representation of the probability density function. Most common distributions in the exponential family are not curved, and many algorithms designed to work with any exponential family implicitly or explicitly assume that the distribution is not curved.

As in the above case of a scalar-valued parameter, the function {rm {J}6})} ou équivalent {rm {J}5})} is automatically determined once the other functions have been chosen, so that the entire distribution is normalized. en outre, comme ci-dessus, both of these functions can always be written as functions of {rm {J}4}} , regardless of the form of the transformation that generates {rm {J}3}} de {rm {J}2},} . Hence an exponential family in its "natural form" (parametrized by its natural parameter) looks like {rm {J}1(xmid {rm {J}0})=h(X),exp {je=1}9{je=1}8}cdot mathbf {je=1}7 (X)-UN({je=1}6}){je=1}5} ou équivalent {je=1}4(xmid {je=1}3})=h(X),g({je=1}2}),exp {je=1}1{je=1}0}cdot mathbf {n}9 (X){n}8} The above forms may sometimes be seen with {n}7}^{n}6}mathbf {n}5 (X)} in place of {n}4}cdot mathbf {n}3 (X),} . These are exactly equivalent formulations, merely using different notation for the dot product.

Vector parameter, vector variable The vector-parameter form over a single scalar-valued random variable can be trivially expanded to cover a joint distribution over a vector of random variables. The resulting distribution is simply the same as the above distribution for a scalar-valued random variable with each occurrence of the scalar x replaced by the vector {n}2 =gauche(X_{n}1,X_{n}0,cdots ,X_{J}9droit)^{J}8}~.} The dimensions k of the random variable need not match the dimension d of the parameter vector, nor (in the case of a curved exponential function) the dimension s of the natural parameter {J}7}} and sufficient statistic T(X) .

The distribution in this case is written as {J}6!la gauche(mathbf {J}5 milieu {J}4}droit)=h(mathbf {J}3 ),exp !la gauche(,somme _{J}2^{J}1eta _{J}0({je}9})T_{je}8(mathbf {je}7 )-UN({je}6}),droit)} Or more compactly as {je}5!la gauche(,mathbf {je}4 milieu {je}3},droit)=h(mathbf {je}2 ),exp !{je}1,{je}0}({symbole gras {eta }9})cdot mathbf {symbole gras {eta }8 (mathbf {symbole gras {eta }7 )-UN({symbole gras {eta }6}),{symbole gras {eta }5} Or alternatively as {symbole gras {eta }4!la gauche(,mathbf {symbole gras {eta }3 milieu {symbole gras {eta }2},droit)= g({symbole gras {eta }1});h(mathbf {symbole gras {eta }0 ),exp !{nu }9,{nu }8}({nu }7})cdot mathbf {nu }6 (mathbf {nu }5 ),{nu }4} Measure-theoretic formulation We use cumulative distribution functions (CDF) in order to encompass both discrete and continuous distributions.

Suppose H is a non-decreasing function of a real variable. Then Lebesgue–Stieltjes integrals with respect to {nu }3}H(mathbf {nu }2 )} are integrals with respect to the reference measure of the exponential family generated by H .

Any member of that exponential family has cumulative distribution function {nu }1}Fleft(,mathbf {nu }0 milieu {symbole gras {eta }9},droit)= exp {symbole gras {eta }8,{symbole gras {eta }7}(thêta )cdot mathbf {symbole gras {eta }6 (mathbf {symbole gras {eta }5 ),-,UN({symbole gras {eta }4}),{symbole gras {eta }3~{symbole gras {eta }2}H(mathbf {symbole gras {eta }1 )~.} H(X) is a Lebesgue–Stieltjes integrator for the reference measure. When the reference measure is finite, it can be normalized and H is actually the cumulative distribution function of a probability distribution. If F is absolutely continuous with a density {symbole gras {eta }0 with respect to a reference measure {rm {J}9}X,} (typically Lebesgue measure), on peut écrire {rm {J}8}F(X)=f(X)~{rm {J}7}X,} . Dans ce cas, H is also absolutely continuous and can be written {rm {J}6}H(X)=h(X),{rm {J}5}X,} so the formulas reduce to that of the previous paragraphs. If F is discrete, then H is a step function (with steps on the support of F).

Alternativement, we can write the probability measure directly as {rm {J}4}mathbf {rm {J}3 milieu {rm {J}2},droit)= exp {rm {J}1,{rm {J}0}(thêta )cdot mathbf {symbole gras {chi }9 (mathbf {symbole gras {chi }8 )-UN({symbole gras {chi }7}),{symbole gras {chi }6~mu ({symbole gras {chi }5}mathbf {symbole gras {chi }4 )~.} for some reference measure {symbole gras {chi }3 .

Interpretation In the definitions above, the functions T(X), η(je), et un(η) were apparently arbitrarily defined. Cependant, these functions play a significant role in the resulting probability distribution.

J(X) is a sufficient statistic of the distribution. For exponential families, the sufficient statistic is a function of the data that holds all information the data x provides with regard to the unknown parameter values. Cela signifie que, for any data sets {symbole gras {chi }2 et {symbole gras {chi }1 , the likelihood ratio is the same, C'est {symbole gras {chi }0)}{symbole gras {eta }9)}}={symbole gras {eta }8)}{symbole gras {eta }7)}}} if T(X) =T(y) . This is true even if x and y are quite distinct – that is, even if {symbole gras {eta }6 . The dimension of T(X) equals the number of parameters of θ and encompasses all of the information regarding the data related to the parameter θ. The sufficient statistic of a set of independent identically distributed data observations is simply the sum of individual sufficient statistics, and encapsulates all the information needed to describe the posterior distribution of the parameters, given the data (and hence to derive any desired estimate of the parameters). (This important property is discussed further below.) η is called the natural parameter. The set of values of η for which the function {symbole gras {eta }5(X;eta )} is integrable is called the natural parameter space. It can be shown that the natural parameter space is always convex. UN(η) is called the log-partition function[b] because it is the logarithm of a normalization factor, without which {symbole gras {eta }4(X;thêta )} would not be a probability distribution: {symbole gras {eta }3h(X),exp(eta (thêta )cdot T(X)),mathrm {symbole gras {eta }2 xright)} The function A important in its own right, because the mean, variance and other moments of the sufficient statistic T(X) can be derived simply by differentiating A(η). Par exemple, because log(X) is one of the components of the sufficient statistic of the gamma distribution, {symbole gras {eta }1} [journal x]} can be easily determined for this distribution using A(η). Technically, this is true because {symbole gras {eta }0 is the cumulant generating function of the sufficient statistic.

Properties Exponential families have a large number of properties that make them extremely useful for statistical analysis. In many cases, it can be shown that only exponential families have these properties. Exemples: Exponential families are the only families with sufficient statistics that can summarize arbitrary amounts of independent identically distributed data using a fixed number of values. (Pitman–Koopman–Darmois theorem) Exponential families have conjugate priors, an important property in Bayesian statistics. The posterior predictive distribution of an exponential-family random variable with a conjugate prior can always be written in closed form (provided that the normalizing factor of the exponential-family distribution can itself be written in closed form).[c] In the mean-field approximation in variational Bayes (used for approximating the posterior distribution in large Bayesian networks), the best approximating posterior distribution of an exponential-family node (a node is a random variable in the context of Bayesian networks) with a conjugate prior is in the same family as the node.[7] Given an exponential family defined by {nu +n}9(xmid theta )=h(X),exp !{nu +n}8,theta cdot T(X)-UN(thêta ),{nu +n}7} , où {nu +n}6 is the parameter space, tel que {nu +n}5 ^{nu +n}4} . Then If {nu +n}3 has nonempty interior in {nu +n}2 ^{nu +n}1} , then given any IID samples {nu +n}0,...,X_{symbole gras {eta }9sim f_{symbole gras {eta }8} , the statistic {symbole gras {eta }7,...,X_{symbole gras {eta }6):=somme _{symbole gras {eta }5^{symbole gras {eta }4J(X_{symbole gras {eta }3)} is a complete statistic for {symbole gras {eta }2 .[8][9] {symbole gras {eta }1 is a minimal statistic for {symbole gras {eta }0 iff for all {rm {J}9,thêta _{rm {J}8in Theta } , et {rm {J}7,X_{rm {J}6} in the support of {rm {J}5 , si {rm {J}4-thêta _{rm {J}3)cdot (J(X_{rm {J}2)-J(X_{rm {J}1))=0} , alors {rm {J}0=thêta _{symbole gras {chi }9} ou {symbole gras {chi }8=x_{symbole gras {chi }7} .[10] Examples It is critical, when considering the examples in this section, to remember the discussion above about what it means to say that a "distribution" is an exponential family, and in particular to keep in mind that the set of parameters that are allowed to vary is critical in determining whether a "distribution" is or is not an exponential family.

The normal, exponentiel, log-normal, gamma, chi-squared, bêta, Dirichlet, Bernoulli, categorical, Poisson, geometric, inverse Gaussian, von Mises and von Mises-Fisher distributions are all exponential families.

Some distributions are exponential families only if some of their parameters are held fixed. The family of Pareto distributions with a fixed minimum bound xm form an exponential family. The families of binomial and multinomial distributions with fixed number of trials n but unknown probability parameter(s) are exponential families. The family of negative binomial distributions with fixed number of failures (alias. stopping-time parameter) r is an exponential family. Cependant, when any of the above-mentioned fixed parameters are allowed to vary, the resulting family is not an exponential family.

As mentioned above, as a general rule, the support of an exponential family must remain the same across all parameter settings in the family. This is why the above cases (par exemple. binomial with varying number of trials, Pareto with varying minimum bound) are not exponential families — in all of the cases, the parameter in question affects the support (notamment, changing the minimum or maximum possible value). For similar reasons, neither the discrete uniform distribution nor continuous uniform distribution are exponential families as one or both bounds vary.

The Weibull distribution with fixed shape parameter k is an exponential family. Unlike in the previous examples, the shape parameter does not affect the support; the fact that allowing it to vary makes the Weibull non-exponential is due rather to the particular form of the Weibull's probability density function (k appears in the exponent of an exponent).

En général, distributions that result from a finite or infinite mixture of other distributions, par exemple. mixture model densities and compound probability distributions, are not exponential families. Examples are typical Gaussian mixture models as well as many heavy-tailed distributions that result from compounding (c'est à dire. infinitely mixing) a distribution with a prior distribution over one of its parameters, par exemple. the Student's t-distribution (compounding a normal distribution over a gamma-distributed precision prior), and the beta-binomial and Dirichlet-multinomial distributions. Other examples of distributions that are not exponential families are the F-distribution, Cauchy distribution, hypergeometric distribution and logistic distribution.

Following are some detailed examples of the representation of some useful distribution as exponential families.

Normal distribution: unknown mean, known variance As a first example, consider a random variable distributed normally with unknown mean μ and known variance σ2. The probability density function is then {symbole gras {chi }6(X;dans )={symbole gras {chi }5{symbole gras {chi }4}}}e ^{-(x-nous )^{symbole gras {chi }3/(2sigma ^{symbole gras {chi }2)}.} This is a single-parameter exponential family, as can be seen by setting {symbole gras {chi }1h_{symbole gras {chi }0(X)&={je=1}9{je=1}8}}}e ^{-x^{je=1}7/(2sigma ^{je=1}6)}\[4pt]T_{je=1}5(X)&={je=1}4{je=1}3}\[4pt]UN_{je=1}2(dans )&={je=1}1}{je=1}0}}\[4pt]eta _{n}9(dans )&={n}8{n}7}.fin{n}6}} If σ = 1 this is in canonical form, as then η(m) = μ.

Normal distribution: unknown mean and unknown variance Next, consider the case of a normal distribution with unknown mean and unknown variance. The probability density function is then {n}5{n}4}}}e ^{-(y-mu )^{n}3/2sigma ^{n}2}.} This is an exponential family which can be written in canonical form by defining {n}1{n}0}&=left[,{J}9{J}8}},~-{J}7{J}6}},droit]\h(y)&={J}5{J}4}}\J(y)&=left(y,y ^{J}3droit)^{J}2}\UN({J}1})&={J}0}{je}9}}+Journal |sigma |=-{je}8^{je}7}{je}6}}+{je}5{je}4}bûche à gauche|{je}3{je}2}}droit|fin{je}1}} Binomial distribution As an example of a discrete exponential family, consider the binomial distribution with known number of trials n. The probability mass function for this distribution is {je}0p^{aligné}9(1-p)^{aligné}8,quad xin {aligné}7.} This can equivalently be written as {aligné}6exp gauche(xlog left({aligné}5{aligné}4}droit)+nlog(1-p)droit),} which shows that the binomial distribution is an exponential family, whose natural parameter is {aligné}3{aligné}2}.} This function of p is known as logit.

Table of distributions The following table shows how to rewrite a number of common distributions as exponential-family distributions with natural parameters. Refer to the flashcards[11] for main exponential families.

For a scalar variable and scalar parameter, the form is as follows: {aligné}1(xmid theta )=h(X)exp {aligné}0eta ({style d'affichage p({symbole gras {eta }9)J(X)-UN({style d'affichage p({symbole gras {eta }8){style d'affichage p({symbole gras {eta }7} For a scalar variable and vector parameter: {style d'affichage p({symbole gras {eta }6(xmid {style d'affichage p({symbole gras {eta }5})=h(X)exp {style d'affichage p({symbole gras {eta }4{style d'affichage p({symbole gras {eta }3}({style d'affichage p({symbole gras {eta }2})cdot mathbf {style d'affichage p({symbole gras {eta }1 (X)-UN({style d'affichage p({symbole gras {eta }0}){X}9} {X}8(xmid {X}7})=h(X)g({X}6})exp {X}5{X}4}({X}3})cdot mathbf {X}2 (X){X}1} For a vector variable and vector parameter: {X}0(mathbf {symbole gras {chi }9 milieu {symbole gras {chi }8})=h(mathbf {symbole gras {chi }7 )exp {symbole gras {chi }6{symbole gras {chi }5}({symbole gras {chi }4})cdot mathbf {symbole gras {chi }3 (mathbf {symbole gras {chi }2 )-UN({symbole gras {chi }1}){symbole gras {chi }0} The above formulas choose the functional form of the exponential-family with a log-partition function {pi }9})} . The reason for this is so that the moments of the sufficient statistics can be calculated easily, simply by differentiating this function. Alternative forms involve either parameterizing this function in terms of the normal parameter {pi }8}} instead of the natural parameter, and/or using a factor {pi }7})} outside of the exponential. The relation between the latter and the former is: {pi }6})=-log g({pi }5})} {pi }4})=e^{-UN({pi }3})}} To convert between the representations involving the two types of parameter, use the formulas below for writing one type of parameter in terms of the other.

The three variants of the categorical distribution and multinomial distribution are due to the fact that the parameters {style d'affichage p_{je}} are constrained, tel que {somme de style d'affichage _{je=1}^{k}p_{je}=1~.} Ainsi, there are only {style d'affichage k-1} independent parameters.

Une variante 1 uses {style d'affichage k} natural parameters with a simple relation between the standard and natural parameters; toutefois, only {style d'affichage k-1} of the natural parameters are independent, and the set of {style d'affichage k} natural parameters is nonidentifiable. The constraint on the usual parameters translates to a similar constraint on the natural parameters. Une variante 2 demonstrates the fact that the entire set of natural parameters is nonidentifiable: Adding any constant value to the natural parameters has no effect on the resulting distribution. Cependant, by using the constraint on the natural parameters, the formula for the normal parameters in terms of the natural parameters can be written in a way that is independent on the constant that is added. Une variante 3 shows how to make the parameters identifiable in a convenient way by setting {displaystyle C=-log p_{k} .} This effectively "pivots" autour de {style d'affichage p_{k}} and causes the last natural parameter to have the constant value of 0. All the remaining formulas are written in a way that does not access {style d'affichage p_{k} } , so that effectively the model has only {style d'affichage k-1} parameters, both of the usual and natural kind.

Variantes 1 et 2 are not actually standard exponential families at all. Rather they are curved exponential families, c'est à dire. il y a {style d'affichage k-1} independent parameters embedded in a {style d'affichage k} -dimensional parameter space.[12] Many of the standard results for exponential families do not apply to curved exponential families. An example is the log-partition function {style d'affichage A(X) } , which has the value of 0 in the curved cases. In standard exponential families, the derivatives of this function correspond to the moments (more technically, the cumulants) of the sufficient statistics, par exemple. the mean and variance. Cependant, a value of 0 suggests that the mean and variance of all the sufficient statistics are uniformly 0, whereas in fact the mean of the {style d'affichage i} th sufficient statistic should be {style d'affichage p_{je} } . (This does emerge correctly when using the form of {style d'affichage A(X) } shown in variant 3.) Moments and cumulants of the sufficient statistic Normalization of the distribution We start with the normalization of the probability distribution. En général, any non-negative function f(X) that serves as the kernel of a probability distribution (the part encoding all dependence on x) can be made into a proper distribution by normalizing: c'est à dire.

{style d'affichage p(X)={frac {1}{Z}}F(X)} où {displaystyle Z=int _{X}F(X),dx.} The factor Z is sometimes termed the normalizer or partition function, based on an analogy to statistical physics.

In the case of an exponential family where {style d'affichage p(X;{symbole gras {eta }})= g({symbole gras {eta }})h(X)e ^{{symbole gras {eta }}cdot mathbf {J} (X)},} the kernel is {style d'affichage K(X)=h(X)e ^{{symbole gras {eta }}cdot mathbf {J} (X)}} and the partition function is {displaystyle Z=int _{X}h(X)e ^{{symbole gras {eta }}cdot mathbf {J} (X)},dx.} Since the distribution must be normalized, Nous avons {displaystyle 1=int _{X}g({symbole gras {eta }})h(X)e ^{{symbole gras {eta }}cdot mathbf {J} (X)},dx=g({symbole gras {eta }})entier _{X}h(X)e ^{{symbole gras {eta }}cdot mathbf {J} (X)},dx=g({symbole gras {eta }})Z.} Autrement dit, {style d'affichage g({symbole gras {eta }})={frac {1}{Z}}} ou équivalent {style d'affichage A({symbole gras {eta }})=-log g({symbole gras {eta }})=log Z.} This justifies calling A the log-normalizer or log-partition function.

Moment-generating function of the sufficient statistic Now, the moment-generating function of T(X) est {style d'affichage M_{J}(tu)equiv E[e ^{u^{Haut }J(X)}mid eta ]=int _{X}h(X)e ^{(eta +u)^{Haut }J(X)-UN(eta )},dx=e^{UN(eta +u)-UN(eta )}} proving the earlier statement that {style d'affichage K(umid eta )=A(eta +u)-UN(eta )} is the cumulant generating function for T.

An important subclass of exponential families are the natural exponential families, which have a similar form for the moment-generating function for the distribution of x.

Differential identities for cumulants In particular, using the properties of the cumulant generating function, {nom de l'opérateur de style d'affichage {E} (T_{j})={frac {partial A(eta )}{partial eta _{j}}}} et {nom de l'opérateur de style d'affichage {cov} la gauche(T_{je}, T_{j}droit)={frac {partiel ^{2}UN(eta )}{partial eta _{je},partial eta _{j}}}.} The first two raw moments and all mixed second moments can be recovered from these two identities. Higher-order moments and cumulants are obtained by higher derivatives. This technique is often useful when T is a complicated function of the data, whose moments are difficult to calculate by integration.

Another way to see this that does not rely on the theory of cumulants is to begin from the fact that the distribution of an exponential family must be normalized, and differentiate. We illustrate using the simple case of a one-dimensional parameter, but an analogous derivation holds more generally.

In the one-dimensional case, Nous avons {style d'affichage p(X)= g(eta )h(X)e ^{eta T(X)}.} This must be normalized, alors {displaystyle 1=int _{X}p(X),dx=int _{X}g(eta )h(X)e ^{eta T(X)},dx=g(eta )entier _{X}h(X)e ^{eta T(X)},dx.} Take the derivative of both sides with respect to η: {style d'affichage {commencer{aligné}0&=g(eta ){frac {ré}{deta }}entier _{X}h(X)e ^{eta T(X)},dx+g'(eta )entier _{X}h(X)e ^{eta T(X)},dx\&=g(eta )entier _{X}h(X)la gauche({frac {ré}{deta }}e ^{eta T(X)}droit),dx+g'(eta )entier _{X}h(X)e ^{eta T(X)},dx\&=g(eta )entier _{X}h(X)e ^{eta T(X)}J(X),dx+g'(eta )entier _{X}h(X)e ^{eta T(X)},dx\&=int _{X}J(X)g(eta )h(X)e ^{eta T(X)},dx+{frac {g'(eta )}{g(eta )}}entier _{X}g(eta )h(X)e ^{eta T(X)},dx\&=int _{X}J(X)p(X),dx+{frac {g'(eta )}{g(eta )}}entier _{X}p(X),dx\&=operatorname {E} [J(X)]+{frac {g'(eta )}{g(eta )}}\&=operatorname {E} [J(X)]+{frac {ré}{deta }}log g(eta )fin{aligné}}} Par conséquent, {nom de l'opérateur de style d'affichage {E} [J(X)]=-{frac {ré}{deta }}log g(eta )={frac {ré}{deta }}UN(eta ).} Exemple 1 As an introductory example, consider the gamma distribution, whose distribution is defined by {style d'affichage p(X)={frac {bêta ^{alpha }}{Gamma (alpha )}}x^{alpha -1}e ^{-beta x}.} Referring to the above table, we can see that the natural parameter is given by {displaystyle eta _{1}=alpha -1,} {displaystyle eta _{2}=-beta ,} the reverse substitutions are {displaystyle alpha =eta _{1}+1,} {displaystyle beta =-eta _{2},} the sufficient statistics are {style d'affichage (journal x,X),} and the log-partition function is {style d'affichage A(eta _{1},eta _{2})=log Gamma (eta _{1}+1)-(eta _{1}+1)Journal(-eta _{2}).} We can find the mean of the sufficient statistics as follows. Première, for η1: {style d'affichage {commencer{aligné}nom de l'opérateur {E} [journal x]&={frac {partial A(eta _{1},eta _{2})}{partial eta _{1}}}={frac {partiel }{partial eta _{1}}}la gauche(log Gamma (eta _{1}+1)-(eta _{1}+1)Journal(-eta _{2})droit)\&=psi (eta _{1}+1)-Journal(-eta _{2})\&=psi (alpha )-log beta ,fin{aligné}}} Où {style d'affichage psi (X)} is the digamma function (derivative of log gamma), and we used the reverse substitutions in the last step.

À présent, for η2: {style d'affichage {commencer{aligné}nom de l'opérateur {E} [X]&={frac {partial A(eta _{1},eta _{2})}{partial eta _{2}}}={frac {partiel }{partial eta _{2}}}la gauche(log Gamma (eta _{1}+1)-(eta _{1}+1)Journal(-eta _{2})droit)\&=-(eta _{1}+1){frac {1}{-eta _{2}}}(-1)={frac {eta _{1}+1}{-eta _{2}}}\&={frac {alpha }{bêta }},fin{aligné}}} again making the reverse substitution in the last step.

To compute the variance of x, we just differentiate again: {style d'affichage {commencer{aligné}nom de l'opérateur {A été} (X)&={frac {partiel ^{2}Aleft(eta _{1},eta _{2}droit)}{partial eta _{2}^{2}}}={frac {partiel }{partial eta _{2}}}{frac {eta _{1}+1}{-eta _{2}}}\&={frac {eta _{1}+1}{eta _{2}^{2}}}\&={frac {alpha }{bêta ^{2}}}.fin{aligné}}} All of these calculations can be done using integration, making use of various properties of the gamma function, but this requires significantly more work.

Exemple 2 As another example consider a real valued random variable X with density {style d'affichage p_{thêta }(X)={frac {theta e^{-X}}{la gauche(1+e ^{-X}droit)^{thêta +1}}}} indexed by shape parameter {displaystyle theta in (0,infime )} (this is called the skew-logistic distribution). The density can be rewritten as {style d'affichage {frac {e ^{-X}}{1+e ^{-X}}}exp gauche(-theta log left(1+e ^{-X}droit)+Journal(thêta )droit)} Notice this is an exponential family with natural parameter {displaystyle eta =-theta ,} sufficient statistic {displaystyle T=log left(1+e ^{-X}droit),} and log-partition function {style d'affichage A(eta )=-log(thêta )=-log(-eta )} So using the first identity, {nom de l'opérateur de style d'affichage {E} (Journal(1+e ^{-X}))=nomopérateur {E} (J)={frac {partial A(eta )}{partial eta }}={frac {partiel }{partial eta }}[-Journal(-eta )]={frac {1}{-eta }}={frac {1}{thêta }},} and using the second identity {nom de l'opérateur de style d'affichage {var} (bûche à gauche(1+e ^{-X}droit))={frac {partiel ^{2}UN(eta )}{partial eta ^{2}}}={frac {partiel }{partial eta }}la gauche[{frac {1}{-eta }}droit]={frac {1}{(-eta )^{2}}}={frac {1}{theta ^{2}}}.} This example illustrates a case where using this method is very simple, but the direct calculation would be nearly impossible.

Exemple 3 The final example is one where integration would be extremely difficult. This is the case of the Wishart distribution, which is defined over matrices. Even taking derivatives is a bit tricky, as it involves matrix calculus, but the respective identities are listed in that article.

From the above table, we can see that the natural parameter is given by {style d'affichage {symbole gras {eta }}_{1}=-{frac {1}{2}}mathbf {V} ^{-1},} {displaystyle eta _{2}={frac {n-p-1}{2}},} the reverse substitutions are {style d'affichage mathbf {V} =-{frac {1}{2}}{{symbole gras {eta }}_{1}}^{-1},} {displaystyle n=2eta _{2}+p+1,} and the sufficient statistics are {style d'affichage (mathbf {X} ,Journal |mathbf {X} |).} The log-partition function is written in various forms in the table, to facilitate differentiation and back-substitution. We use the following forms: {style d'affichage A({symbole gras {eta }}_{1},n)=-{frac {n}{2}}Journal |-{symbole gras {eta }}_{1}|+log Gamma _{p}la gauche({frac {n}{2}}droit),} {style d'affichage A(mathbf {V} ,eta _{2})=gauche(eta _{2}+{frac {p+1}{2}}droit)(plog 2+log |mathbf {V} |)+log Gamma _{p}la gauche(eta _{2}+{frac {p+1}{2}}droit).} Expectation of X (associated with η1) To differentiate with respect to η1, we need the following matrix calculus identity: {style d'affichage {frac {partial log |amathbf {X} |}{mathbf partiel {X} }}=(mathbf {X} ^{-1})^{rm {J}}} Alors: {style d'affichage {commencer{aligné}nom de l'opérateur {E} [mathbf {X} ]&={frac {partial Aleft({symbole gras {eta }}_{1},cdots à droite)}{partiel {symbole gras {eta }}_{1}}}\&={frac {partiel }{partiel {symbole gras {eta }}_{1}}}la gauche[-{frac {n}{2}}Journal |-{symbole gras {eta }}_{1}|+log Gamma _{p}la gauche({frac {n}{2}}droit)droit]\&=-{frac {n}{2}}({symbole gras {eta }}_{1}^{-1})^{rm {J}}\&={frac {n}{2}}(-{symbole gras {eta }}_{1}^{-1})^{rm {J}}\&=n(mathbf {V} )^{rm {J}}\&=nmathbf {V} fin{aligné}}} The last line uses the fact that V is symmetric, and therefore it is the same when transposed.

Expectation of log |X| (associated with η2) À présent, for η2, we first need to expand the part of the log-partition function that involves the multivariate gamma function: {displaystyle log Gamma _{p}(un)=log left(pi ^{frac {p(p-1)}{4}}produit _{j=1}^{p}Gamma à gauche(a+{frac {1-j}{2}}droit)droit)={frac {p(p-1)}{4}}log pi +sum _{j=1}^{p}log Gamma left[a+{frac {1-j}{2}}droit]} We also need the digamma function: {style d'affichage psi (X)={frac {ré}{dx}}log Gamma (X).} Alors: {style d'affichage {commencer{aligné}nom de l'opérateur {E} [Journal |mathbf {X} |]&={frac {partial Aleft(ldots ,eta _{2}droit)}{partial eta _{2}}}\&={frac {partiel }{partial eta _{2}}}la gauche[-la gauche(eta _{2}+{frac {p+1}{2}}droit)(plog 2+log |mathbf {V} |)+log Gamma _{p}la gauche(eta _{2}+{frac {p+1}{2}}droit)droit]\&={frac {partiel }{partial eta _{2}}}la gauche[la gauche(eta _{2}+{frac {p+1}{2}}droit)(plog 2+log |mathbf {V} |)+{frac {p(p-1)}{4}}log pi +sum _{j=1}^{p}log Gamma left(eta _{2}+{frac {p+1}{2}}+{frac {1-j}{2}}droit)droit]\&=plog 2+log |mathbf {V} |+somme _{j=1}^{p}psi left(eta _{2}+{frac {p+1}{2}}+{frac {1-j}{2}}droit)\&=plog 2+log |mathbf {V} |+somme _{j=1}^{p}psi left({frac {n-p-1}{2}}+{frac {p+1}{2}}+{frac {1-j}{2}}droit)\&=plog 2+log |mathbf {V} |+somme _{j=1}^{p}psi left({frac {n+1-j}{2}}droit)fin{aligné}}} This latter formula is listed in the Wishart distribution article. Both of these expectations are needed when deriving the variational Bayes update equations in a Bayes network involving a Wishart distribution (which is the conjugate prior of the multivariate normal distribution).

Computing these formulas using integration would be much more difficult. The first one, par exemple, would require matrix integration.

Entropy Relative entropy The relative entropy (Kullback–Leibler divergence, KL divergence) of two distributions in an exponential family has a simple expression as the Bregman divergence between the natural parameters with respect to the log-normalizer.[13] The relative entropy is defined in terms of an integral, while the Bregman divergence is defined in terms of a derivative and inner product, and thus is easier to calculate and has a closed-form expression (assuming the derivative has a closed-form expression). Plus loin, the Bregman divergence in terms of the natural parameters and the log-normalizer equals the Bregman divergence of the dual parameters (expectation parameters), in the opposite order, for the convex conjugate function.[14] Fixing an exponential family with log-normalizer {style d'affichage A} (with convex conjugate {style d'affichage A^{*}} ), writing {style d'affichage P_{UN,thêta }} for the distribution in this family corresponding a fixed value of the natural parameter {thêta de style d'affichage } (writing {displaystyle theta '} for another value, et avec {style d'affichage eta ,eta '} for the corresponding dual expectation/moment parameters), writing KL for the KL divergence, et {style d'affichage B_{UN}} for the Bregman divergence, the divergences are related as: {style d'affichage {rm {{KL}(P_{UN,thêta }parallel P_{UN,theta '})=B_{UN}(theta 'parallel theta )=B_{Un ^{*}}(eta parallel eta ').}}} The KL divergence is conventionally written with respect to the first parameter, while the Bregman divergence is conventionally written with respect to the second parameter, and thus this can be read as "the relative entropy is equal to the Bregman divergence defined by the log-normalizer on the swapped natural parameters", or equivalently as "equal to the Bregman divergence defined by the dual to the log-normalizer on the expectation parameters".

Maximum-entropy derivation Exponential families arise naturally as the answer to the following question: what is the maximum-entropy distribution consistent with given constraints on expected values?

The information entropy of a probability distribution dF(X) can only be computed with respect to some other probability distribution (ou, plus généralement, a positive measure), and both measures must be mutually absolutely continuous. Par conséquent, we need to pick a reference measure dH(X) with the same support as dF(X).

The entropy of dF(X) relative to dH(X) est {style d'affichage S[dFmid dH]=-int {frac {dF}{dH}}Journal {frac {dF}{dH}},dH} ou {style d'affichage S[dFmid dH]=int log {frac {dH}{dF}},dF} where dF/dH and dH/dF are Radon–Nikodym derivatives. The ordinary definition of entropy for a discrete distribution supported on a set I, à savoir {displaystyle S=-sum _{je dans je}p_{je}log p_{je}} assumes, though this is seldom pointed out, that dH is chosen to be the counting measure on I.

Consider now a collection of observable quantities (random variables) Ti. The probability distribution dF whose entropy with respect to dH is greatest, subject to the conditions that the expected value of Ti be equal to ti, is an exponential family with dH as reference measure and (T1, ..., Tn) as sufficient statistic.

The derivation is a simple variational calculation using Lagrange multipliers. Normalization is imposed by letting T0 = 1 be one of the constraints. The natural parameters of the distribution are the Lagrange multipliers, and the normalization factor is the Lagrange multiplier associated to T0.

For examples of such derivations, see Maximum entropy probability distribution.

Role in statistics Classical estimation: sufficiency According to the Pitman–Koopman–Darmois theorem, among families of probability distributions whose domain does not vary with the parameter being estimated, only in exponential families is there a sufficient statistic whose dimension remains bounded as sample size increases.

Less tersely, suppose Xk, (where k = 1, 2, 3, ... n) are independent, identically distributed random variables. Only if their distribution is one of the exponential family of distributions is there a sufficient statistic T(X1, ..., Xn) whose number of scalar components does not increase as the sample size n increases; the statistic T may be a vector or a single scalar number, but whatever it is, its size will neither grow nor shrink when more data are obtained.

As a counterexample if these conditions are relaxed, the family of uniform distributions (either discrete or continuous, with either or both bounds unknown) has a sufficient statistic, namely the sample maximum, sample minimum, and sample size, but does not form an exponential family, as the domain varies with the parameters.

Bayesian estimation: conjugate distributions Exponential families are also important in Bayesian statistics. In Bayesian statistics a prior distribution is multiplied by a likelihood function and then normalised to produce a posterior distribution. In the case of a likelihood which belongs to an exponential family there exists a conjugate prior, which is often also in an exponential family. A conjugate prior π for the parameter {style d'affichage {symbole gras {eta }}} of an exponential family {style d'affichage f(xmid {symbole gras {eta }})=h(X)exp gauche({symbole gras {eta }}^{rm {J}}mathbf {J} (X)-UN({symbole gras {eta }})droit)} est donné par {style d'affichage p_{pi }({symbole gras {eta }}milieu {symbole gras {chi }},nu )=f({symbole gras {chi }},nu )exp gauche({symbole gras {eta }}^{rm {J}}{symbole gras {chi }}-nu A({symbole gras {eta }})droit),} ou équivalent {style d'affichage p_{pi }({symbole gras {eta }}milieu {symbole gras {chi }},nu )=f({symbole gras {chi }},nu )g({symbole gras {eta }})^{nu }exp gauche({symbole gras {eta }}^{rm {J}}{symbole gras {chi }}droit),qquad {symbole gras {chi }}en mathbb {R} ^{s}} where s is the dimension of {style d'affichage {symbole gras {eta }}} et {displaystyle nu >0} et {style d'affichage {symbole gras {chi }}} are hyperparameters (parameters controlling parameters). {style d'affichage non } corresponds to the effective number of observations that the prior distribution contributes, et {style d'affichage {symbole gras {chi }}} corresponds to the total amount that these pseudo-observations contribute to the sufficient statistic over all observations and pseudo-observations. {style d'affichage f({symbole gras {chi }},nu )} is a normalization constant that is automatically determined by the remaining functions and serves to ensure that the given function is a probability density function (c'est à dire. it is normalized). {style d'affichage A({symbole gras {eta }})} and equivalently {style d'affichage g({symbole gras {eta }})} are the same functions as in the definition of the distribution over which π is the conjugate prior.

A conjugate prior is one which, when combined with the likelihood and normalised, produces a posterior distribution which is of the same type as the prior. Par exemple, if one is estimating the success probability of a binomial distribution, then if one chooses to use a beta distribution as one's prior, the posterior is another beta distribution. This makes the computation of the posterior particularly simple. De la même manière, if one is estimating the parameter of a Poisson distribution the use of a gamma prior will lead to another gamma posterior. Conjugate priors are often very flexible and can be very convenient. Cependant, if one's belief about the likely value of the theta parameter of a binomial is represented by (dire) a bimodal (two-humped) prior distribution, then this cannot be represented by a beta distribution. It can however be represented by using a mixture density as the prior, here a combination of two beta distributions; this is a form of hyperprior.

An arbitrary likelihood will not belong to an exponential family, and thus in general no conjugate prior exists. The posterior will then have to be computed by numerical methods.

To show that the above prior distribution is a conjugate prior, we can derive the posterior.

Première, assume that the probability of a single observation follows an exponential family, parameterized using its natural parameter: {style d'affichage p_{F}(xmid {symbole gras {eta }})=h(X)g({symbole gras {eta }})exp gauche({symbole gras {eta }}^{rm {J}}mathbf {J} (X)droit)} Alors, for data {style d'affichage mathbf {X} =(X_{1},ldots ,X_{n})} , the likelihood is computed as follows: {style d'affichage p(mathbf {X} milieu {symbole gras {eta }})=gauche(produit _{je=1}^{n}h(X_{je})droit)g({symbole gras {eta }})^{n}exp gauche({symbole gras {eta }}^{rm {J}}somme _{je=1}^{n}mathbf {J} (X_{je})droit)} Alors, for the above conjugate prior: {style d'affichage {commencer{aligné}p_{pi }({symbole gras {eta }}milieu {symbole gras {chi }},nu )&=f({symbole gras {chi }},nu )g({symbole gras {eta }})^{nu }exp({symbole gras {eta }}^{rm {J}}{symbole gras {chi }})propto g({symbole gras {eta }})^{nu }exp({symbole gras {eta }}^{rm {J}}{symbole gras {chi }})fin{aligné}}} We can then compute the posterior as follows: {style d'affichage {commencer{aligné}p({symbole gras {eta }}mid mathbf {X} ,{symbole gras {chi }},nu )&propto p(mathbf {X} milieu {symbole gras {eta }})p_{pi }({symbole gras {eta }}milieu {symbole gras {chi }},nu )\&=left(produit _{je=1}^{n}h(X_{je})droit)g({symbole gras {eta }})^{n}exp gauche({symbole gras {eta }}^{rm {J}}somme _{je=1}^{n}mathbf {J} (X_{je})droit)F({symbole gras {chi }},nu )g({symbole gras {eta }})^{nu }exp({symbole gras {eta }}^{rm {J}}{symbole gras {chi }})\&propto g({symbole gras {eta }})^{n}exp gauche({symbole gras {eta }}^{rm {J}}somme _{je=1}^{n}mathbf {J} (X_{je})droit)g({symbole gras {eta }})^{nu }exp({symbole gras {eta }}^{rm {J}}{symbole gras {chi }})\&propto g({symbole gras {eta }})^{nu +n}exp gauche({symbole gras {eta }}^{rm {J}}la gauche({symbole gras {chi }}+somme _{je=1}^{n}mathbf {J} (X_{je})droit)droit)fin{aligné}}} The last line is the kernel of the posterior distribution, c'est à dire.

{style d'affichage p({symbole gras {eta }}mid mathbf {X} ,{symbole gras {chi }},nu )=p_{pi }la gauche({symbole gras {eta }}la gauche|~{symbole gras {chi }}+somme _{je=1}^{n}mathbf {J} (X_{je}),nu +nright.right)} This shows that the posterior has the same form as the prior.

The data X enters into this equation only in the expression {style d'affichage mathbf {J} (mathbf {X} )=somme _{je=1}^{n}mathbf {J} (X_{je}),} which is termed the sufficient statistic of the data. C'est-à-dire, the value of the sufficient statistic is sufficient to completely determine the posterior distribution. The actual data points themselves are not needed, and all sets of data points with the same sufficient statistic will have the same distribution. This is important because the dimension of the sufficient statistic does not grow with the data size — it has only as many components as the components of {style d'affichage {symbole gras {eta }}} (de manière équivalente, the number of parameters of the distribution of a single data point).