In mathematics, a generating function is a way of encoding an infinite sequence of numbers (a_{n}) by treating them as the coefficients of a power series. This formal power series is the generating function. Unlike an ordinary series, this formal series is allowed to diverge, meaning that the generating function is not always a true function and the "variable" is actually an indeterminate. Generating functions were first introduced by Abraham de Moivre in 1730, in order to solve the general linear recurrence problem.^{[1]} One can generalize to formal series in more than one indeterminate, to encode information about arrays of numbers indexed by several natural numbers.
There are various types of generating functions, including ordinary generating functions, exponential generating functions, Lambert series, Bell series, and Dirichlet series; definitions and examples are given below. Every sequence in principle has a generating function of each type (except that Lambert and Dirichlet series require indices to start at 1 rather than 0), but the ease with which they can be handled may differ considerably. The particular generating function, if any, that is most useful in a given context will depend upon the nature of the sequence and the details of the problem being addressed.
Generating functions are often expressed in closed form (rather than as a series), by some expression involving operations defined for formal series. These expressions in terms of the indeterminate x may involve arithmetic operations, differentiation with respect to x and composition with (i.e., substitution into) other generating functions; since these operations are also defined for functions, the result looks like a function of x. Indeed, the closed form expression can often be interpreted as a function that can be evaluated at (sufficiently small) concrete values of x, and which has the formal series as its series expansion; this explains the designation "generating functions". However such interpretation is not required to be possible, because formal series are not required to give a convergent series when a nonzero numeric value is substituted for x. Also, not all expressions that are meaningful as functions of x are meaningful as expressions designating formal series; for example, negative and fractional powers of x are examples of functions that do not have a corresponding formal power series.
Generating functions are not functions in the formal sense of a mapping from a domain to a codomain. Generating functions are sometimes called generating series,^{[2]} in that a series of terms can be said to be the generator of its sequence of term coefficients.
The ordinary generating function of a sequence a_{n} is
When the term generating function is used without qualification, it is usually taken to mean an ordinary generating function.
If a_{n} is the probability mass function of a discrete random variable, then its ordinary generating function is called a probabilitygenerating function.
The ordinary generating function can be generalized to arrays with multiple indices. For example, the ordinary generating function of a twodimensional array a_{m, n} (where n and m are natural numbers) is
The exponential generating function of a sequence a_{n} is
Exponential generating functions are generally more convenient than ordinary generating functions for combinatorial enumeration problems that involve labelled objects.^{[3]}
The Poisson generating function of a sequence a_{n} is
The Lambert series of a sequence a_{n} is
The Lambert series coefficients in the power series expansions for integers are related by the divisor sum . The main article provides several more classical, or at least wellknown examples related to special arithmetic functions in number theory. Note that in a Lambert series the index n starts at 1, not at 0, as the first term would otherwise be undefined.
The Bell series of a sequence a_{n} is an expression in terms of both an indeterminate x and a prime p and is given by^{[4]}
Formal Dirichlet series are often classified as generating functions, although they are not strictly formal power series. The Dirichlet series generating function of a sequence a_{n} is^{[5]}
The Dirichlet series generating function is especially useful when a_{n} is a multiplicative function, in which case it has an Euler product expression^{[6]} in terms of the function's Bell series
If a_{n} is a Dirichlet character then its Dirichlet series generating function is called a Dirichlet Lseries. We also have a relation between the pair of coefficients in the Lambert series expansions above and their DGFs. Namely, we can prove that if and only if where is the Riemann zeta function.^{[7]}
The idea of generating functions can be extended to sequences of other objects. Thus, for example, polynomial sequences of binomial type are generated by
where p_{n}(x) is a sequence of polynomials and f(t) is a function of a certain form. Sheffer sequences are generated in a similar way. See the main article generalized Appell polynomials for more information.
Polynomials are a special case of ordinary generating functions, corresponding to finite sequences, or equivalently sequences that vanish after a certain point. These are important in that many finite sequences can usefully be interpreted as generating functions, such as the Poincaré polynomial and others.
A key generating function is that of the constant sequence 1, 1, 1, 1, 1, 1, 1, 1, 1, ..., whose ordinary generating function is the geometric series
The lefthand side is the Maclaurin series expansion of the righthand side. Alternatively, the equality can be justified by multiplying the power series on the left by 1 − x, and checking that the result is the constant power series 1 (in other words, that all coefficients except the one of x^{0} are equal to 0). Moreover, there can be no other power series with this property. The lefthand side therefore designates the multiplicative inverse of 1 − x in the ring of power series.
Expressions for the ordinary generating function of other sequences are easily derived from this one. For instance, the substitution x → ax gives the generating function for the geometric sequence 1, a, a^{2}, a^{3}, ... for any constant a:
(The equality also follows directly from the fact that the lefthand side is the Maclaurin series expansion of the righthand side.) In particular,
One can also introduce regular "gaps" in the sequence by replacing x by some power of x, so for instance for the sequence 1, 0, 1, 0, 1, 0, 1, 0, .... one gets the generating function
By squaring the initial generating function, or by finding the derivative of both sides with respect to x and making a change of running variable n → n + 1, one sees that the coefficients form the sequence 1, 2, 3, 4, 5, ..., so one has
and the third power has as coefficients the triangular numbers 1, 3, 6, 10, 15, 21, ... whose term n is the binomial coefficient , so that
More generally, for any nonnegative integer k and nonzero real value a, it is true that
Note that, since
one can find the ordinary generating function for the sequence 0, 1, 4, 9, 16, ... of square numbers by linear combination of binomialcoefficient generating sequences:
We may also expand alternately to generate this same sequence of squares as a sum of derivatives of the geometric series in the following form:
By induction, we can similarly show for positive integers that ^{[8]}^{[9]}
where denote the Stirling numbers of the second kind and where the generating function , so that we can form the analogous generating functions over the integral th powers generalizing the result in the square case above. In particular, since we can write , we can apply a wellknown finite sum identity involving the Stirling numbers to obtain that^{[10]}
The ordinary generating function of a sequence can be expressed as a rational function (the ratio of two finitedegree polynomials) if and only if the sequence is a linear recursive sequence with constant coefficients; this generalizes the examples above. Conversely, every sequence generated by a fraction of polynomials satisfies a linear recurrence with constant coefficients; these coefficients are identical to the coefficients of the fraction denominator polynomial (so they can be directly read off). This observation shows it is easy to solve for generating functions of sequences defined by a linear finite difference equation with constant coefficients, and then hence, for explicit closedform formulas for the coefficients of these generating functions. The prototypical example here is to derive Binet's formula for the Fibonacci numbers via generating function techniques.
We also notice that the class of rational generating functions precisely corresponds to the generating functions that enumerate quasipolynomial sequences of the form ^{[11]}
where the reciprocal roots, , are fixed scalars and where is a polynomial in for all .
In general, Hadamard products of rational functions produce rational generating functions. Similarly, if is a bivariate rational generating function, then its corresponding diagonal generating function, , is algebraic. For example, if we let ^{[12]}
then this generating function's diagonal coefficient generating function is given by the wellknown OGF formula
This result is computed in many ways, including Cauchy's integral formula or contour integration, taking complex residues, or by direct manipulations of formal power series in two variables.
Multiplication of ordinary generating functions yields a discrete convolution (the Cauchy product) of the sequences. For example, the sequence of cumulative sums (compare to the slightly more general Euler–Maclaurin formula)
of a sequence with ordinary generating function G(a_{n}; x) has the generating function
because 1/(1 − x) is the ordinary generating function for the sequence (1, 1, ...). See also the section on convolutions in the applications section of this article below for further examples of problem solving with convolutions of generating functions and interpretations.
For integers , we have the following two analogous identities for the modified generating functions enumerating the shifted sequence variants of and , respectively:
We have the following respective power series expansions for the first derivative of a generating function and its integral:
The differentiation–multiplication operation of the second identity can be repeated times to multiply the sequence by , but that requires alternating between differentiation and multiplication. If instead doing differentiations in sequence, the effect is to multiply by the ^{th} falling factorial:
Using the Stirling numbers of the second kind, that can be turned into another formula for multiplying by as follows (see the main article on generating function transformations):
A negativeorder reversal of this sequence powers formula corresponding to the operation of repeated integration is defined by the zeta series transformation and its generalizations defined as a derivativebased transformation of generating functions, or alternately termwise by an performing an integral transformation on the sequence generating function. Related operations of performing fractional integration on a sequence generating function are discussed here.
In this section we give formulas for generating functions enumerating the sequence given an ordinary generating function where , , and (see the main article on transformations). For , this is simply the familiar decomposition of a function into even and odd parts (i.e., even and odd powers):
More generally, suppose that and that denotes the ^{th} primitive root of unity. Then, as an application of the discrete Fourier transform, we have the formula^{[13]}
For integers , another useful formula providing somewhat reversed floored arithmetic progressions — effectively repeating each coefficient times — are generated by the identity^{[14]}
A formal power series (or function) is said to be holonomic if it satisfies a linear differential equation of the form ^{[15]}
where the coefficients are in the field of rational functions, . Equivalently, is holonomic if the vector space over spanned by the set of all of its derivatives is finite dimensional.
Since we can clear denominators if need be in the previous equation, we may assume that the functions, are polynomials in . Thus we can see an equivalent condition that a generating function is holonomic if its coefficients satisfy a Precurrence of the form
for all large enough and where the are fixed finitedegree polynomials in . In other words, the properties that a sequence be Precursive and have a holonomic generating function are equivalent. Holonomic functions are closed under the Hadamard product operation on generating functions.
The functions , , , , , the dilogarithm function , the generalized hypergeometric functions and the functions defined by the power series and the nonconvergent are all holonomic. Examples of Precursive sequences with holonomic generating functions include and , where sequences such as and are not Precursive due to the nature of singularities in their corresponding generating functions. Similarly, functions with infinitelymany singularities such as , , and are not holonomic functions.
Tools for processing and working with Precursive sequences in Mathematica include the software packages provided for noncommercial use on the RISC Combinatorics Group algorithmic combinatorics software site. Despite being mostly closedsource, particularly powerful tools in this software suite are provided by the Guess package for guessing Precurrences for arbitrary input sequences (useful for experimental mathematics and exploration) and the Sigma package which is able to find Precurrences for many sums and solve for closedform solutions to Precurrences involving generalized harmonic numbers.^{[16]} Other packages listed on this particular RISC site are targeted at working with holonomic generating functions specifically. (Depending on how in depth this article gets on the topic, there are many, many other examples of useful software tools that can be listed here or on this page in another section.)
When the series converges absolutely,
is the discretetime Fourier transform of the sequence a_{0}, a_{1}, ....
In calculus, often the growth rate of the coefficients of a power series can be used to deduce a radius of convergence for the power series. The reverse can also hold; often the radius of convergence for a generating function can be used to deduce the asymptotic growth of the underlying sequence.
For instance, if an ordinary generating function G(a_{n}; x) that has a finite radius of convergence of r can be written as
where each of A(x) and B(x) is a function that is analytic to a radius of convergence greater than r (or is entire), and where B(r) ≠ 0 then
using the Gamma function, a binomial coefficient, or a multiset coefficient.
Often this approach can be iterated to generate several terms in an asymptotic series for a_{n}. In particular,
The asymptotic growth of the coefficients of this generating function can then be sought via the finding of A, B, α, β, and r to describe the generating function, as above.
Similar asymptotic analysis is possible for exponential generating functions. With an exponential generating function, it is a_{n}/n! that grows according to these asymptotic formulae.
As derived above, the ordinary generating function for the sequence of squares is
With r = 1, α = 0, β = 3, A(x) = 0, and B(x) = x(x+1), we can verify that the squares grow as expected, like the squares:
The ordinary generating function for the Catalan numbers is
With r = 1/4, α = 1, β = −1/2, A(x) = 1/2, and B(x) = −1/2, we can conclude that, for the Catalan numbers,
One can define generating functions in several variables for arrays with several indices. These are called multivariate generating functions or, sometimes, super generating functions. For two variables, these are often called bivariate generating functions.
For instance, since is the ordinary generating function for binomial coefficients for a fixed n, one may ask for a bivariate generating function that generates the binomial coefficients for all k and n. To do this, consider as itself a series, in n, and find the generating function in y that has these as coefficients. Since the generating function for is
the generating function for the binomial coefficients is:
Expansions of (formal) Jacobitype and Stieltjestype continued fractions (Jfractions and Sfractions, respectively) whose rational convergents represent order accurate power series are another way to express the typically divergent ordinary generating functions for many special one and twovariate sequences. The particular form of the Jacobitype continued fractions (Jfractions) are expanded as in the following equation and have the next corresponding power series expansions with respect to for some specific, applicationdependent component sequences, and , where denotes the formal variable in the second power series expansion given below:^{[17]}
The coefficients of , denoted in shorthand by , in the previous equations correspond to matrix solutions of the equations
where , for , if , and where for all integers , we have an addition formula relation given by
For (though in practice when ), we can define the rational convergents to the infinite Jfraction, , expanded by
componentwise through the sequences, and , defined recursively by
Moreover, the rationality of the convergent function, for all implies additional finite difference equations and congruence properties satisfied by the sequence of , and for if then we have the congruence
for nonsymbolic, determinate choices of the parameter sequences, and , when , i.e., when these sequences do not implicitly depend on an auxiliary parameter such as , , or as in the examples contained in the table below.
The next table provides examples of closedform formulas for the component sequences found computationally (and subsequently proved correct in the cited references ^{[18]}) in several special cases of the prescribed sequences, , generated by the general expansions of the Jfractions defined in the first subsection. Here we define and the parameters , and to be indeterminates with respect to these expansions, where the prescribed sequences enumerated by the expansions of these Jfractions are defined in terms of the qPochhammer symbol, Pochhammer symbol, and the binomial coefficients.
 

Note that the radii of convergence of these series corresponding to the definition of the Jacobitype Jfractions given above are in general different from that of the corresponding power series expansions defining the ordinary generating functions of these sequences.
Generating functions for the sequence of square numbers a_{n} = n^{2} are:
As an example of a Lambert series identity not given in the main article, we can show that for we have that ^{[19]}
where we have the special case identity for the generating function of the divisor function, , given by
using the Riemann zeta function.
The sequence a_{k} generated by a Dirichlet series generating function (DGF) corresponding to:
where is the Riemann zeta function, has the ordinary generating function:
Multivariate generating functions arise in practice when calculating the number of contingency tables of nonnegative integers with specified row and column totals. Suppose the table has r rows and c columns; the row sums are and the column sums are . Then, according to I. J. Good,^{[20]} the number of such tables is the coefficient of
in
In the bivariate case, nonpolynomial double sum examples of sotermed "double" or "super" generating functions of the form include the following twovariable generating functions for the binomial coefficients, the Stirling numbers, and the Eulerian numbers:^{[21]}
Generating functions give us several methods to manipulate sums and to establish identities between sums.
The simplest case occurs when . We then know that for the corresponding ordinary generating functions.
For example, we can manipulate , where are the harmonic numbers. Let be the ordinary generating function of the harmonic numbers. Then
and thus
Using , convolution with the numerator yields
which can also be written as
As another example of using generating functions to relate sequences and manipulate sums, for an arbitrary sequence we define the two sequences of sums
for all , and seek to express the second sums in terms of the first. We suggest an approach by generating functions.
First, we use the binomial transform to write the generating function for the first sum as
Since the generating function for the sequence is given by , we may write the generating function for the second sum defined above in the form
In particular, we may write this modified sum generating function in the form of
for , , , and where .
Finally, it follows that we may express the second sums through the first sums in the following form:
In this example, we reformulate a generating function example given in Section 7.3 of Concrete Mathematics (see also Section 7.1 of the same reference for pretty pictures of generating function series). In particular, suppose that we seek the total number of ways (denoted ) to tile a rectangle with unmarked domino pieces. Let the auxiliary sequence, , be defined as the number of ways to cover a rectangleminuscorner section of the full rectangle. We seek to use these definitions to give a closed form formula for without breaking down this definition further to handle the cases of vertical versus horizontal dominoes. Notice that the ordinary generating functions for our two sequences correspond to the series
If we consider the possible configurations that can be given starting from the left edge of the rectangle, we are able to express the following mutually dependent, or mutually recursive, recurrence relations for our two sequences when defined as above where , , , and :
Since we have that for all integers , the indexshifted generating functions satisfy (incidentally, we also have a corresponding formula when given by ), we can use the initial conditions specified above and the previous two recurrence relations to see that we have the next two equations relating the generating functions for these sequences given by
which then implies by solving the system of equations (and this is the particular trick to our method here) that
Thus by performing algebraic simplifications to the sequence resulting from the second partial fractions expansions of the generating function in the previous equation, we find that and that
for all integers . We also note that the same shifted generating function technique applied to the secondorder recurrence for the Fibonacci numbers is the prototypical example of using generating functions to solve recurrence relations in one variable already covered, or at least hinted at, in the subsection on rational functions given above.
A discrete convolution of the terms in two formal power series turns a product of generating functions into a generating function enumerating a convolved sum of the original sequence terms (see Cauchy product).
Multiplication of generating functions, or convolution of their underlying sequences, can correspond to a notion of independent events in certain counting and probability scenarios. For example, if we adopt the notational convention that the probability generating function, or pgf, of a random variable is denoted by , then we can show that for any two random variables ^{[22]}
if and are independent. Similarly, the number of ways to pay cents in coin denominations of values in the set (i.e., in pennies, nickels, dimes, quarters, and half dollars, respectively) is generated by the product
and moreover, if we allow the cents to be paid in coins of any positive integer denomination, we arrive at the generating for the number of such combinations of change being generated by the partition function generating function expanded by the infinite qPochhammer symbol product of .
An example where convolutions of generating functions are useful allows us to solve for a specific closedform function representing the ordinary generating function for the Catalan numbers, . In particular, this sequence has the combinatorial interpretation as being the number of ways to insert parentheses into the product so that the order of multiplication is completely specified. For example, which corresponds to the two expressions and . It follows that the sequence satisfies a recurrence relation given by
and so has a corresponding convolved generating function, , satisfying
Since , we then arrive at a formula for this generating function given by
Note that the first equation implicitly defining above implies that
which then leads to another "simple" (as in of form) continued fraction expansion of this generating function.
A fan of order is defined to be a graph on the vertices with edges connected according to the following rules: Vertex is connected by a single edge to each of the other vertices, and vertex is connected by a single edge to the next vertex for all .^{[23]} There is one fan of order one, three fans of order two, eight fans of order three, and so on. A spanning tree is a subgraph of a graph which contains all of the original vertices and which contains enough edges to make this subgraph connected, but not so many edges that there is a cycle in the subgraph. We ask how many spanning trees of a fan of order are possible for each .
As an observation, we may approach the question by counting the number of ways to join adjacent sets of vertices. For example, when , we have that , which is a sum over the fold convolutions of the sequence for . More generally, we may write a formula for this sequence as
from which we see that the ordinary generating function for this sequence is given by the next sum of convolutions as
from which we are able to extract an exact formula for the sequence by taking the partial fraction expansion of the last generating function.
Sometimes the sum is complicated, and it is not always easy to evaluate. The "Free Parameter" method is another method (called "snake oil" by H. Wilf) to evaluate these sums.
Both methods discussed so far have as limit in the summation. When n does not appear explicitly in the summation, we may consider as a “free” parameter and treat as a coefficient of , change the order of the summations on and , and try to compute the inner sum.
For example, if we want to compute
we can treat as a "free" parameter, and set
Interchanging summation (“snake oil”) gives
Now the inner sum is . Thus
Then we obtain
We say that two generating functions (power series) are congruent modulo , written if their coefficients are congruent modulo for all , i.e., for all relevant cases of the integers (note that we need not assume that is an integer here—it may very well be polynomialvalued in some indeterminate , for example). If the "simpler" righthandside generating function, , is a rational function of , then the form of this sequences suggests that the sequence is eventually periodic modulo fixed particular cases of integervalued . For example, we can prove that the Euler numbers, , satisfy the following congruence modulo :^{[24]}
One of the most useful, if not downright powerful, methods of obtaining congruences for sequences enumerated by special generating functions modulo any integers (i.e., not only prime powers ) is given in the section on continued fraction representations of (even nonconvergent) ordinary generating functions by Jfractions above. We cite one particular result related to generating series expanded through a representation by continued fraction from Lando's Lectures on Generating Functions as follows:
Generating functions also have other uses in proving congruences for their coefficients. We cite the next two specific examples deriving special case congruences for the Stirling numbers of the first kind and for the partition function (mathematics) which show the versatility of generating functions in tackling problems involving integer sequences.
The main article on the Stirling numbers generated by the finite products
provides an overview of the congruences for these numbers derived strictly from properties of their generating function as in Section 4.6 of Wilf's stock reference Generatingfunctionology. We repeat the basic argument and notice that when reduces modulo , these finite product generating functions each satisfy
which implies that the parity of these Stirling numbers matches that of the binomial coefficient
and consequently shows that is even whenever .
Similarly, we can reduce the righthandside products defining the Stirling number generating functions modulo to obtain slightly more complicated expressions providing that
In this example, we pull in some of the machinery of infinite products whose power series expansions generate the expansions of many special functions and enumerate partition functions. In particular, we recall that the partition function is generated by the reciprocal infinite qPochhammer symbol product (or zPochhammer product as the case may be) given by
This partition function satisfies many known congruence properties, which notably include the following results though there are still many open questions about the forms of related integer congruences for the function:^{[25]}
We show how to use generating functions and manipulations of congruences for formal power series to give a highly elementary proof of the first of these congruences listed above.
First, we observe that the binomial coefficient generating function, , satisfies that each of its coefficients are divisible by with the exception of those which correspond to the powers of , all of which otherwise have a remainder of modulo . Thus we may write
which in particular shows us that
Hence, we easily see that divides each coefficient of in the infinite product expansions of
Finally, since we may write the generating function for the partition function as
we may equate the coefficients of in the previous equations to prove our desired congruence result, namely that, for all .
There are a number of transformations of generating functions that provide other applications (see the main article). A transformation of a sequence's ordinary generating function (OGF) provides a method of converting the generating function for one sequence into a generating function enumerating another. These transformations typically involve integral formulas involving a sequence OGF (see integral transformations) or weighted sums over the higherorder derivatives of these functions (see derivative transformations).
Generating function transformations can come into play when we seek to express a generating function for the sums
in the form of involving the original sequence generating function. For example, if the sums , then the generating function for the modified sum expressions is given by ^{[26]} (see also the binomial transform and the Stirling transform).
There are also integral formulas for converting between a sequence's OGF, , and its exponential generating function, or EGF, , and vice versa given by
provided that these integrals converge for appropriate values of .
Generating functions are used to:
Examples of polynomial sequences generated by more complex generating functions include:
Other sequences generated by more complex generating functions:
Knuth's article titled "Convolution Polynomials"^{[27]} defines a generalized class of convolution polynomial sequences by their special generating functions of the form
for some analytic function with a power series expansion such that . We say that a family of polynomials, , forms a convolution family if and if the following convolution condition holds for all and for all :
We see that for nonidentically zero convolution families, this definition is equivalent to requiring that the sequence have an ordinary generating function of the first form given above.
A sequence of convolution polynomials defined in the notation above has the following properties:
For a fixed nonzero parameter , we have modified generating functions for these convolution polynomial sequences given by
where is implicitly defined by a functional equation of the form . Moreover, we can use matrix methods (as in the reference) to prove that given two convolution polynomial sequences, and , with respective corresponding generating functions, and , then for arbitrary we have the identity
Examples of convolution polynomial sequences include the binomial power series, , sotermed tree polynomials, the Bell numbers, , the Laguerre polynomials, and the Stirling convolution polynomials.
An initial listing of special mathematical series is found here. A number of useful and special sequence generating functions are found in Section 5.4 and 7.4 of Concrete Mathematics and in Section 2.5 of Wilf's Generatingfunctionology. Other special generating functions of note include the entries in the next table, which is by no means complete.^{[28]}
Formal power series  Generatingfunction formula  Notes 

is a firstorder harmonic number  
is a Bernoulli number  
is a Fibonacci number and  
denotes the rising factorial, or Pochhammer symbol and some integer  
is the polylogarithm function and is a generalized harmonic number for  
is a Stirling number of the second kind and where the individual terms in the expansion satisfy  
The twovariable case is given by  
George Polya writes in Mathematics and plausible reasoning:
In combinatorial mathematics, the Bell numbers count the possible partitions of a set. These numbers have been studied by mathematicians since the 19th century, and their roots go back to medieval Japan, but they are named after Eric Temple Bell, who wrote about them in the 1930s.
Starting with B0 = B1 = 1, the first few Bell numbers are:
1, 1, 2, 5, 15, 52, 203, 877, 4140, 21147, 115975, 678570, 4213597, 27644437, 190899322, 1382958545, 10480142147, 82864869804, 682076806159, 5832742205057, ... (sequence A000110 in the OEIS).The nth of these numbers, Bn, counts the number of different ways to partition a set that has exactly n elements, or equivalently, the number of equivalence relations on it.
Outside of mathematics, the same number also counts the number of different rhyme schemes for nline poems.As well as appearing in counting problems, these numbers have a different interpretation, as moments of probability distributions. In particular, Bn is the nth moment of a Poisson distribution with mean 1.
Binomial transformIn combinatorics, the binomial transform is a sequence transformation (i.e., a transform of a sequence) that computes its forward differences. It is closely related to the Euler transform, which is the result of applying the binomial transform to the sequence associated with its ordinary generating function.
Canonical transformationIn Hamiltonian mechanics, a canonical transformation is a change of canonical coordinates (q, p, t) → (Q, P, t) that preserves the form of Hamilton's equations. This is sometimes known as form invariance. It need not preserve the form of the Hamiltonian itself. Canonical transformations are useful in their own right, and also form the basis for the Hamilton–Jacobi equations (a useful method for calculating conserved quantities) and Liouville's theorem (itself the basis for classical statistical mechanics).
Since Lagrangian mechanics is based on generalized coordinates, transformations of the coordinates q → Q do not affect the form of Lagrange's equations and, hence, do not affect the form of Hamilton's equations if we simultaneously change the momentum by a Legendre transformation into
Therefore, coordinate transformations (also called point transformations) are a type of canonical transformation. However, the class of canonical transformations is much broader, since the old generalized coordinates, momenta and even time may be combined to form the new generalized coordinates and momenta. Canonical transformations that do not include the time explicitly are called restricted canonical transformations (many textbooks consider only this type).
For clarity, we restrict the presentation here to calculus and classical mechanics. Readers familiar with more advanced mathematics such as cotangent bundles, exterior derivatives and symplectic manifolds should read the related symplectomorphism article. (Canonical transformations are a special case of a symplectomorphism.) However, a brief introduction to the modern mathematical description is included at the end of this article.
Characteristic function (probability theory)In probability theory and statistics, the characteristic function of any realvalued random variable completely defines its probability distribution. If a random variable admits a probability density function, then the characteristic function is the Fourier transform of the probability density function. Thus it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or cumulative distribution functions. There are particularly simple results for the characteristic functions of distributions defined by the weighted sums of random variables.
In addition to univariate distributions, characteristic functions can be defined for vector or matrixvalued random variables, and can also be extended to more generic cases.
The characteristic function always exists when treated as a function of a realvalued argument, unlike the momentgenerating function. There are relations between the behavior of the characteristic function of a distribution and properties of the distribution, such as the existence of moments and the existence of a density function.
CumulantIn probability theory and statistics, the cumulants κn of a probability distribution are a set of quantities that provide an alternative to the moments of the distribution. The moments determine the cumulants in the sense that any two probability distributions whose moments are identical will have identical cumulants as well, and similarly the cumulants determine the moments.
The first cumulant is the mean, the second cumulant is the variance, and the third cumulant is the same as the third central moment. But fourth and higherorder cumulants are not equal to central moments. In some cases theoretical treatments of problems in terms of cumulants are simpler than those using moments. In particular, when two or more random variables are statistically independent, the nthorder cumulant of their sum is equal to the sum of their nthorder cumulants. As well, the third and higherorder cumulants of a normal distribution are zero, and it is the only distribution with this property.
Just as for moments, where joint moments are used for collections of random variables, it is possible to define joint cumulants.
Enumerative combinatoricsEnumerative combinatorics is an area of combinatorics that deals with the number of ways that certain patterns can be formed. Two examples of this type of problem are counting combinations and counting permutations. More generally, given an infinite collection of finite sets S_{i} indexed by the natural numbers, enumerative combinatorics seeks to describe a counting function which counts the number of objects in S_{n} for each n. Although counting the number of elements in a set is a rather broad mathematical problem, many of the problems that arise in applications have a relatively simple combinatorial description. The twelvefold way provides a unified framework for counting permutations, combinations and partitions.
The simplest such functions are closed formulas, which can be expressed as a composition of elementary functions such as factorials, powers, and so on. For instance, as shown below, the number of different possible orderings of a deck of n cards is f(n) = n!. The problem of finding a closed formula is known as algebraic enumeration, and frequently involves deriving a recurrence relation or generating function and using this to arrive at the desired closed form.
Often, a complicated closed formula yields little insight into the behavior of the counting function as the number of counted objects grows. In these cases, a simple asymptotic approximation may be preferable. A function is an asymptotic approximation to if as . In this case, we write
Extended negative binomial distributionIn probability and statistics the extended negative binomial distribution is a discrete probability distribution extending the negative binomial distribution. It is a truncated version of the negative binomial distribution for which estimation methods have been studied.In the context of actuarial science, the distribution appeared in its general form in a paper by K. Hess, A. Liewald and K.D. Schmidt when they characterized all distributions for which the extended Panjer recursion works. For the case m = 1, the distribution was already discussed by Willmot and put into a parametrized family with the logarithmic distribution and the negative binomial distribution by H.U. Gerber.
Factorial moment generating functionIn probability theory and statistics, the factorial moment generating function of the probability distribution of a realvalued random variable X is defined as
for all complex numbers t for which this expected value exists. This is the case at least for all t on the unit circle , see characteristic function. If X is a discrete random variable taking values only in the set {0,1, ...} of nonnegative integers, then is also called probabilitygenerating function of X and is welldefined at least for all t on the closed unit disk .
The factorial moment generating function generates the factorial moments of the probability distribution. Provided exists in a neighbourhood of t = 1, the nth factorial moment is given by
where the Pochhammer symbol (x)_{n} is the falling factorial
(Many mathematicians, especially in the field of special functions, use the same notation to represent the rising factorial.)
Formula for primesIn number theory, a formula for primes is a formula generating the prime numbers, exactly and without exception. No such formula which is efficiently computable is known. A number of constraints are known, showing what such a "formula" can and cannot be.
Gamma/Gompertz distributionIn probability and statistics, the Gamma/Gompertz distribution is a continuous probability distribution. It has been used as an aggregatelevel model of customer lifetime and a model of mortality risks.
Momentgenerating functionIn probability theory and statistics, the momentgenerating function of a realvalued random variable is an alternative specification of its probability distribution. Thus, it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or cumulative distribution functions. There are particularly simple results for the momentgenerating functions of distributions defined by the weighted sums of random variables. However, not all random variables have momentgenerating functions.
As its name implies, the moment generating function can be used to compute a distribution’s moments: the nth moment about 0 is the nth derivative of the momentgenerating function, evaluated at 0.
In addition to realvalued distributions (univariate distributions), momentgenerating functions can be defined for vector or matrixvalued random variables, and can even be extended to more general cases.
The momentgenerating function of a realvalued distribution does not always exist, unlike the characteristic function. There are relations between the behavior of the momentgenerating function of a distribution and properties of the distribution, such as the existence of moments.
Narayana numberIn combinatorics, the Narayana numbers N(n, k), n = 1, 2, 3 ..., 1 ≤ k ≤ n, form a triangular array of natural numbers, called Narayana triangle, that occur in various counting problems. They are named after Canadian mathematician T. V. Narayana (1930–1987).
Natural exponential familyIn probability and statistics, a natural exponential family (NEF) is a class of probability distributions that is a special case of an exponential family (EF). Every distribution possessing a momentgenerating function is a member of a natural exponential family, and the use of such distributions simplifies the theory and computation of generalized linear models.
Pentagonal pyramidal numberA pentagonal pyramidal number is a figurate number that represents the number of objects in a pyramid with a pentagonal base. The n^{th} pentagonal pyramidal number is equal to the sum of the first n pentagonal numbers.
The first few pentagonal pyramidal numbers are:
1, 6, 18, 40, 75, 126, 196, 288, 405, 550, 726, 936, 1183, 1470, 1800, 2176, 2601, 3078, 3610, 4200, 4851, 5566, 6348, 7200, 8125, 9126 (sequence A002411 in the OEIS).
The formula for the n^{th} pentagonal pyramidal number is
so the n^{th} pentagonal pyramidal number is the average of n^{2} and n^{3}. The n^{th} pentagonal pyramidal number is also n times the n^{th} triangular number.
The generating function for the pentagonal pyramidal numbers is
In probability theory, the probability generating function of a discrete random variable is a power series representation (the generating function) of the probability mass function of the random variable. Probability generating functions are often employed for their succinct description of the sequence of probabilities Pr(X = i) in the probability mass function for a random variable X, and to make available the welldeveloped theory of power series with nonnegative coefficients.
Uquadratic distributionIn probability theory and statistics, the Uquadratic distribution is a continuous probability distribution defined by a unique convex quadratic function with lower limit a and upper limit b.
In probability theory and statistics, the continuous uniform distribution or rectangular distribution is a family of symmetric probability distributions such that for each member of the family, all intervals of the same length on the distribution's support are equally probable. The support is defined by the two parameters, a and b, which are its minimum and maximum values. The distribution is often abbreviated U(a,b). It is the maximum entropy probability distribution for a random variable X under no constraint other than that it is contained in the distribution's support.
Weibull distributionIn probability theory and statistics, the Weibull distribution is a continuous probability distribution. It is named after Swedish mathematician Waloddi Weibull, who described it in detail in 1951, although it was first identified by Fréchet (1927) and first applied by Rosin & Rammler (1933) to describe a particle size distribution.
Zeta distributionIn probability theory and statistics, the zeta distribution is a discrete probability distribution. If X is a zetadistributed random variable with parameter s, then the probability that X takes the integer value k is given by the probability mass function
where ζ(s) is the Riemann zeta function (which is undefined for s = 1).
The multiplicities of distinct prime factors of X are independent random variables.
The Riemann zeta function being the sum of all terms for positive integer k, it appears thus as the normalization of the Zipf distribution. The terms "Zipf distribution" and the "zeta distribution" are often used interchangeably. But note that while the Zeta distribution is a probability distribution by itself, it is not associated to the Zipf's law with same exponent. See also Yule–Simon distribution
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