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| [[Image:Boxplot vs PDF.svg|thumb|350px|[[Boxplot]] and probability density function of a [[normal distribution]] {{nowrap|''N''(0, ''σ''<sup>2</sup>)}}.]]
| | Hello, I'm Archer, a 30 year old from Kobenhavn K, Denmark.<br>My hobbies include (but are not limited to) College football, Reading and watching Grey's Anatomy.<br><br>my site ... [http://jeaniesbookreviews.com/behind-beautiful-forevers-katherine-boo/ how to Get free fifa 15 coins] |
| In [[probability theory]], a '''probability density function''' ('''pdf'''), or '''density''' of a [[continuous random variable]], is a [[Function (mathematics)|function]] that describes the relative likelihood for this random variable to take on a given value. The probability of the random variable falling within a particular range of values is given by the [[integral]] of this variable’s density over that range—that is, it is given by the area under the density function but above the horizontal axis and between the lowest and greatest values of the range. The probability density function is nonnegative everywhere, and its integral over the entire space is equal to one.
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| The terms "''probability distribution function''"<ref>[http://planetmath.org/?method=png&from=objects&id=2884&op=getobj Probability distribution function] PlanetMath</ref> and "''probability function''"<ref>[http://mathworld.wolfram.com/ProbabilityFunction.html Probability Function] at Mathworld </ref> have also sometimes been used to denote the probability density function. However, this use is not standard among probabilists and statisticians. In other sources, "probability distribution function" may be used when the [[probability distribution]] is defined as a function over general sets of values, or it may refer to the [[cumulative distribution function]], or it may be a [[probability mass function]] rather than the density. Further confusion of terminology exists because ''density function'' has also been used for what is here called the "probability mass function".<ref>Ord, J.K. (1972) ''Families of Frequency Distributions'', Griffin. ISBN 0-85264-137-0 (for example, Table 5.1 and Example 5.4)</ref>
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| ==Absolutely continuous univariate distributions==
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| A probability density function is most commonly associated with [[Continuous probability distribution|absolutely continuous]] [[univariate distribution]]s. A [[random variable]] ''X'' has density ''f<sub>X</sub>'', where ''f<sub>X</sub>'' is a non-negative [[Lebesgue integration|Lebesgue-integrable]] function, if:
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| :<math> \Pr [a \le X \le b] = \int_a^b f_X(x) \, dx .</math>
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| Hence, if ''F<sub>X</sub>'' is the [[cumulative distribution function]] of ''X'', then:
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| :<math>F_X(x) = \int_{-\infty}^x f_X(u) \, du ,</math>
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| and (if ''f<sub>X</sub>'' is continuous at ''x'')
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| :<math> f_X(x) = \frac{d}{dx} F_X(x) .</math>
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| Intuitively, one can think of ''f<sub>X</sub>''(''x'') d''x'' as being the probability of ''X'' falling within the infinitesimal [[interval (mathematics)|interval]] [''x'', ''x'' + d''x''].
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| ==Formal definition==
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| (''This definition may be extended to any probability distribution using the [[measure theory|measure-theoretic]] [[probability axioms|definition of probability]].'')
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| A [[random variable]] ''X'' with values in a [[measurable space]] <math>(\mathcal{X}, \mathcal{A})</math>
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| (usually '''R'''<sup>n</sup> with the [[Borel set]]s as measurable subsets) has as [[probability distribution#Formal definition|probability distribution]] the measure ''X''<sub>∗</sub>''P'' on <math>(\mathcal{X}, \mathcal{A})</math>: the '''density''' of ''X'' with respect to a reference measure ''μ'' on <math>(\mathcal{X}, \mathcal{A})</math> is the [[Radon–Nikodym derivative]]:
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| :<math>f = \frac{d X_*P}{d \mu} .</math>
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| That is, ''f'' is any measurable function with the property that:
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| :<math>\Pr [X \in A ] = \int_{X^{-1}A} \, d P = \int_A f \, d \mu</math>
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| for any measurable set <math>A \in \mathcal{A}</math>.
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| ===Discussion===
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| In the [[#Continuous univariate random variable|continuous univariate case above]], the reference measure is the [[Lebesgue measure]]. The [[probability mass function]] of a [[discrete random variable]] is the density with respect to the [[counting measure]] over the sample space (usually the set of [[integer]]s, or some subset thereof).
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| Note that it is not possible to define a density with reference to an arbitrary measure (e.g. one can't choose the counting measure as a reference for a continuous random variable). Furthermore, when it does exist, the density is [[almost everywhere]] unique.
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| ==Further details==
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| Unlike a probability, a probability density function can take on values greater than one; for example, the [[Uniform distribution (continuous)|uniform distribution]] on the interval [0, ½] has probability density ''f''(''x'') = 2 for 0 ≤ ''x'' ≤ ½ and ''f''(''x'') = 0 elsewhere.
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| The standard [[normal distribution]] has probability density
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| : <math>
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| f(x) = \frac{1}{\sqrt{2\pi}}\; e^{-x^2/2}.
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| </math>
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| If a random variable ''X'' is given and its distribution admits a probability density function ''f'', then the [[expected value]] of ''X'' (if the expected value exists) can be calculated as
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| : <math>
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| \operatorname{E}[X] = \int_{-\infty}^\infty x\,f(x)\,dx.
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| </math>
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| Not every probability distribution has a density function: the distributions of [[discrete random variable]]s do not; nor does the [[Cantor distribution]], even though it has no discrete component, i.e., does not assign positive probability to any individual point.
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| A distribution has a density function if and only if its [[cumulative distribution function]] ''F''(''x'') is [[absolute continuity|absolutely continuous]]. In this case: ''F'' is [[almost everywhere]] [[derivative|differentiable]], and its derivative can be used as probability density:
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| : <math>
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| \frac{d}{dx}F(x) = f(x).
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| </math>
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| If a probability distribution admits a density, then the probability of every one-point set {''a''} is zero; the same holds for finite and countable sets.
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| Two probability densities ''f'' and ''g'' represent the same [[probability distribution]] precisely if they differ only on a set of [[Lebesgue measure|Lebesgue]] [[measure zero]].
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| In the field of [[statistical physics]], a non-formal reformulation of the relation above between the derivative of the [[cumulative distribution function]] and the probability density function is generally used as the definition of the probability density function. This alternate definition is the following:
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| If ''dt'' is an infinitely small number, the probability that ''X'' is included within the interval (''t'', ''t'' + ''dt'') is equal to ''f''(''t'') ''dt'', or:
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| : <math>
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| \Pr(t<X<t+dt) = f(t)\,dt.
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| </math>
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| ==Link between discrete and continuous distributions==
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| It is possible to represent certain discrete random variables as well as random variables involving both a continuous and a discrete part with a generalized probability density function, by using the [[Dirac delta function]]. For example, let us consider a binary discrete [[random variable]] having the [[Rademacher distribution]]—that is, taking −1 or 1 for values, with probability ½ each. The density of probability associated with this variable is:
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| :<math>f(t) = \frac{1}{2}(\delta(t+1)+\delta(t-1)).</math>
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| More generally, if a discrete variable can take ''n'' different values among real numbers, then the associated probability density function is:
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| :<math>f(t) = \sum_{i=1}^np_i\, \delta(t-x_i),</math>
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| where ''x''<sub>1</sub>, …, ''x<sub>n</sub>'' are the discrete values accessible to the variable and ''p''<sub>1</sub>, …, ''p<sub>n</sub>'' are the probabilities associated with these values.
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| This substantially unifies the treatment of discrete and continuous probability distributions. For instance, the above expression allows for determining statistical characteristics of such a discrete variable (such as its [[mean]], its [[variance]] and its [[kurtosis]]), starting from the formulas given for a continuous distribution of the probability.
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| == Families of densities ==
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| It is common for probability density functions (and [[probability mass function]]s) to
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| be parametrized—that is, to be characterized by unspecified [[parameter]]s. For example, the [[normal distribution]] is parametrized in terms of the [[mean]] and the [[variance]], denoted by <math>\mu</math> and <math>\sigma^2</math> respectively, giving the family of densities
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| : <math>
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| f(x;\mu,\sigma^2) = \frac{1}{\sigma\sqrt{2\pi}} e^{ -\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^2 }.
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| </math>
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| It is important to keep in mind the difference between the [[Domain of a function|domain]] of a family of densities and the parameters of the family. Different values of the parameters describe different distributions of different [[random variable]]s on the same [[sample space]] (the same set of all possible values of the variable); this sample space is the domain of the family of random variables that this family of distributions describes. A given set of parameters describes a single distribution within the family sharing the functional form of the density. From the perspective of a given distribution, the parameters are constants, and terms in a density function that contain only parameters, but not variables, are part of the [[normalization factor]] of a distribution (the multiplicative factor that ensures that the area under the density—the probability of ''something'' in the domain occurring— equals 1). This normalization factor is outside the [[kernel (statistics)|kernel]] of the distribution.
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| Since the parameters are constants, reparametrizing a density in terms of different parameters, to give a characterization of a different random variable in the family, means simply substituting the new parameter values into the formula in place of the old ones. Changing the domain of a probability density, however, is trickier and requires more work: see the section below on change of variables.
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| == Densities associated with multiple variables ==<!-- This section is linked from [[Sufficiency (statistics)]] -->
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| For continuous [[random variable]]s ''X''<sub>1</sub>, …, ''X<sub>n</sub>'', it is also possible to define a probability density function associated to the set as a whole, often called '''joint probability density function'''. This density function is defined as a function of the ''n'' variables, such that, for any domain ''D'' in the ''n''-dimensional space of the values of the variables ''X''<sub>1</sub>, …, ''X<sub>n</sub>'', the probability that a realisation of the set variables falls inside the domain ''D'' is
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| :<math>\Pr \left( X_1,\cdots,X_N \isin D \right)
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| = \int_D f_{X_1,\cdots,X_N}(x_1,\cdots,x_N)\,dx_1 \cdots dx_N.</math>
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| If ''F''(''x''<sub>1</sub>, …, ''x''<sub>''n''</sub>) = Pr(''X''<sub>1</sub> ≤ ''x''<sub>1</sub>, …, ''X''<sub>''n''</sub> ≤ ''x''<sub>''n''</sub>) is the [[cumulative distribution function]] of the vector (''X''<sub>1</sub>, …, ''X''<sub>''n''</sub>), then the joint probability density function can be computed as a partial derivative
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| : <math>
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| f(x) = \frac{\partial^n F}{\partial x_1 \cdots \partial x_n} \bigg|_x
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| </math>
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| ===Marginal densities===
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| For ''i''=1, 2, …,''n'', let ''f<sub>X<sub>i</sub></sub>''(''x<sub>i</sub>'') be the probability density function associated with variable ''X<sub>i</sub>'' alone. This is called the “marginal” density function, and can be deduced from the probability density associated with the random variables ''X''<sub>1</sub>, …, ''X<sub>n</sub>'' by integrating on all values of the ''n'' − 1 other variables:
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| :<math>f_{X_i}(x_i) = \int f(x_1,\cdots,x_n)\, dx_1 \cdots dx_{i-1}\,dx_{i+1}\cdots dx_n .</math>
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| ===Independence===
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| Continuous random variables ''X''<sub>1</sub>, …, ''X<sub>n</sub>'' admitting a joint density are all [[statistical independence|independent]] from each other if and only if
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| :<math>f_{X_1,\cdots,X_n}(x_1,\cdots,x_n) = f_{X_1}(x_1)\cdots f_{X_n}(x_n).</math>
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| ===Corollary===
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| If the joint probability density function of a vector of ''n'' random variables can be factored into a product of ''n'' functions of one variable
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| :<math>f_{X_1,\cdots,X_n}(x_1,\cdots,x_n) = f_1(x_1)\cdots f_n(x_n),</math>
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| (where each ''f<sub>i</sub>'' is not necessarily a density) then the ''n'' variables in the set are all [[statistical independence|independent]] from each other, and the marginal probability density function of each of them is given by
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| :<math>f_{X_i}(x_i) = \frac{f_i(x_i)}{\int f_i(x)\,dx}.</math>
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| ===Example===
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| This elementary example illustrates the above definition of multidimensional probability density functions in the simple case of a function of a set of two variables. Let us call <math>\vec R</math> a 2-dimensional random vector of coordinates (''X'', ''Y''): the probability to obtain <math>\vec R</math> in the quarter plane of positive ''x'' and ''y'' is
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| :<math>\Pr \left( X > 0, Y > 0 \right)
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| = \int_0^\infty \int_0^\infty f_{X,Y}(x,y)\,dx\,dy.</math>
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| ==Dependent variables and change of variables==
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| If the probability density function of a random variable ''X'' is given as ''f<sub>X</sub>''(''x''), it is possible (but often not necessary; see below) to calculate the probability density function of some variable {{nowrap|''Y {{=}} g''(''X'')}}. This is also called a “change of variable” and is in practice used to generate a random variable of arbitrary shape {{nowrap|''f''<sub>''g''(''X'')</sub> {{=}} ''f<sub>Y</sub>''}} using a known (for instance uniform) random number generator.
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| If the function ''g'' is [[Monotonic function|monotonic]], then the resulting density function is
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| : <math>f_Y(y) = \left| \frac{d}{dy} (g^{-1}(y)) \right| \cdot f_X(g^{-1}(y)).</math>
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| Here ''g''<sup>−1</sup> denotes the [[inverse function]].
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| This follows from the fact that the probability contained in a differential area must be invariant under change of variables. That is,
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| : <math>\left| f_Y(y)\, dy\right| = \left| f_X(x)\, dx\right|,</math>
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| or
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| : <math>f_Y(y) = \left| \frac{dx}{dy} \right| f_X(x) = \left| \frac{d}{dy} (x) \right| f_X(x) = \left| \frac{d}{dy} (g^{-1}(y)) \right|f_X(g^{-1}(y)).</math>
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| For functions which are not monotonic the probability density function for ''y'' is
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| :<math>\sum_{k=1}^{n(y)} \left| \frac{d}{dy} g^{-1}_{k}(y) \right| \cdot f_X(g^{-1}_{k}(y))</math>
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| where ''n''(''y'') is the number of solutions in ''x'' for the equation {{nowrap|''g''(''x'') {{=}} ''y''}}, and ''g''<sup>−1</sup><sub>''k''</sub>(''y'') are these solutions.
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| It is tempting to think that in order to find the expected value ''E''(''g''(''X'')) one must first find the probability density ''f''<sub>''g''(''X'')</sub> of the new random variable {{nowrap|''Y {{=}} g''(''X'')}}. However, rather than computing
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| :<math> \operatorname E(g(X)) = \int_{-\infty}^\infty y f_{g(X)}(y)\,dy, </math>
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| one may find instead
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| :<math>\operatorname E(g(X)) = \int_{-\infty}^\infty g(x) f_X(x)\,dx.</math>
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| The values of the two integrals are the same in all cases in which both ''X'' and ''g''(''X'') actually have probability density functions. It is not necessary that ''g'' be a [[one-to-one function]]. In some cases the latter integral is computed much more easily than the former.
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| ===Multiple variables===
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| The above formulas can be generalized to variables (which we will again call ''y'') depending on more than one other variable. ''f''(''x''<sub>1</sub>, …, ''x''<sub>''n''</sub>) shall denote the probability density function of the variables that ''y'' depends on, and the dependence shall be {{nowrap|''y {{=}} g''(''x''<sub>1</sub>, …, ''x''<sub>''n''</sub>)}}. Then, the resulting density function is{{Citation needed|date=October 2013}}
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| :<math> \int\limits_{y = g(x_1, \cdots, x_n)} \frac{f(x_1,\cdots, x_n)}{\sqrt{\sum_{j=1}^n \frac{\partial g}{\partial x_j}(x_1, \cdots, x_n)^2}} \; dV</math> | |
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| where the integral is over the entire (''n''-1)-dimensional solution of the subscripted equation and the symbolic ''dV'' must be replaced by a parametrization of this solution for a particular calculation; the variables ''x''<sub>1</sub>, …, ''x''<sub>''n''</sub> are then of course functions of this parametrization.
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| This derives from the following, perhaps more intuitive representation: Suppose '''''x''''' is an ''n''-dimensional random variable with joint density ''f''. If {{nowrap|'''''y''''' {{=}} ''H''('''''x''''')}}, where ''H'' is a [[bijective]], [[differentiable]] function, then '''''y''''' has density ''g'':
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| : <math>g(\mathbf{y}) = f(\mathbf{x})\left\vert \det\left(\frac{d\mathbf{x}}{d\mathbf{y}}\right)\right \vert</math>
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| with the differential regarded as the [[Jacobian matrix and determinant|Jacobian]] of the inverse of ''H'', evaluated at '''''y'''''.
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| Using the delta-function (and assuming independence) the same result is formulated as follows.
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| If the probability density function of independent random variables ''X<sub>i</sub>'', {{nowrap|''i'' {{=}} 1, 2, …''n''}} are given as ''f<sub>X<sub>i</sub></sub>''(''x<sub>i</sub>''), it is possible to calculate the probability density function of some variable {{nowrap|''Y {{=}} G''(''X''<sub>1</sub>, ''X''<sub>2</sub>, …''X<sub>n</sub>'')}}. The following formula establishes a connection between the probability density function of ''Y'' denoted by ''f<sub>Y</sub>''(''y'') and ''f<sub>X<sub>i</sub></sub>''(''x<sub>i</sub>'') using the [[Dirac delta]] function:
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| :<math>f_Y(y) = \int_{-\infty}^\infty \int_{-\infty}^\infty \cdots \int_{-\infty}^\infty f_{X_1}(x_1)f_{X_2}(x_2) \cdots f_{X_n}(x_n)\delta(y-G(x_1,x_2,\cdots, x_n))\,dx_1\,dx_2\,\cdots dx_n</math>
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| ==Sums of independent random variables==
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| {{See also|Convolution|List of convolutions of probability distributions}}
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| :''Not to be confused with [[Mixture distribution]]''
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| The probability density function of the sum of two [[statistical independence|independent]] random variables ''U'' and ''V'', each of which has a probability density function, is the [[convolution]] of their separate density functions:
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| :<math>
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| f_{U+V}(x) = \int_{-\infty}^\infty f_U(y) f_V(x - y)\,dy
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| = \left( f_{U} * f_{V} \right) (x)
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| </math>
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| It is possible to generalize the previous relation to a sum of N independent random variables, with densities ''U''<sub>1</sub>, …, ''U<sub>N</sub>'':
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| :<math>
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| f_{U_{1} + \cdots + U_{N}}(x)
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| = \left( f_{U_{1}} * \cdots * f_{U_{N}} \right) (x)
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| </math>
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| This can be derived from a two-way change of variables involving ''Y=U+V'' and ''Z=V'', similarly to the example below for the quotient of independent random variables.
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| ==Products and quotients of independent random variables==
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| {{See also|Product distribution|Ratio distribution}}
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| Given two independent random variables ''U'' and ''V'', each of which has a probability density function, the density of the product ''Y''=''UV'' and quotient ''Y''=''U''/''V'' can be computed by a change of variables.
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| ===Example: Quotient distribution===
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| To compute the quotient ''Y''=''U''/''V'' of two independent random variables ''U'' and ''V'', define the following transformation:
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| :<math>Y=U/V</math>
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| :<math>Z=V</math>
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| Then, the joint density ''p(Y,Z)'' can be computed by a change of variables from ''U,V'' to ''Y,Z'', and ''Y'' can be derived by [[marginalizing out]] ''Z'' from the joint density.
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| The inverse transformation is
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| :<math>U = YZ</math>
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| :<math>V = Z</math>
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| The [[Jacobian matrix]] <math>J(U,V|Y,Z)</math> of this transformation is
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| :<math>
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| \begin{vmatrix}
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| \frac{\partial U}{\partial Y} & \frac{\partial U}{\partial Z} \\
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| \frac{\partial V}{\partial Y} & \frac{\partial V}{\partial Z} \\
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| \end{vmatrix}
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| =
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| \begin{vmatrix}
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| Z & Y \\
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| 0 & 1 \\
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| \end{vmatrix}
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| = |Z| .
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| </math>
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| Thus:
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| :<math>p(Y,Z) = p(U,V)\,J(U,V|Y,Z) = p(U)\,p(V)\,J(U,V|Y,Z) = p_U(YZ)\,p_V(Z)\, |Z| .</math>
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| And the distribution of ''Y'' can be computed by [[marginalizing out]] ''Z'':
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| :<math>p(Y) = \int_{-\infty}^{\infty} p_U(YZ)\,p_V(Z)\, |Z| \, dZ</math>
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| Note that this method crucially requires that the transformation from ''U,V'' to ''Y,Z'' be [[bijective]]. The above transformation meets this because ''Z'' can be mapped directly back to ''V'', and for a given ''V'' the quotient ''U/V'' is [[monotonic]]. This is similarly the case for the sum ''U+V'', difference ''U-V'' and product ''UV''.
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| Exactly the same method can be used to compute the distribution of other functions of multiple independent random variables.
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| ===Example: Quotient of two standard normals===
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| Given two [[standard normal distribution|standard normal]] variables ''U'' and ''V'', the quotient can be computed as follows. First, the variables have the following density functions:
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| :<math>p(U) = \frac{1}{\sqrt{2\pi}} e^{-U^2/2}</math>
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| :<math>p(V) = \frac{1}{\sqrt{2\pi}} e^{-V^2/2}</math>
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| We transform as described above:
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| :<math>Y=U/V</math>
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| :<math>Z=V</math>
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| This leads to:
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| :<math>
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| \begin{align}
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| p(Y) &= \int_{-\infty}^{\infty} p_U(YZ)\,p_V(Z)\, |Z| \, dZ \\
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| &= \int_{-\infty}^{\infty} \frac{1}{\sqrt{2\pi}} e^{-Y^2Z^2/2} \frac{1}{\sqrt{2\pi}} e^{-Z^2/2} |Z| \, dZ \\
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| &= \int_{-\infty}^{\infty} \frac{1}{\sqrt{2\pi}} e^{-Y^2Z^2/2} \frac{1}{\sqrt{2\pi}} e^{-Z^2/2} |Z| \, dZ \\
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| &= \int_{-\infty}^{\infty} \frac{1}{2\pi} e^{-(Y^2+1)Z^2/2} |Z| \, dZ \\
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| &= 2\int_{0}^{\infty} \frac{1}{2\pi} e^{-(Y^2+1)Z^2/2} Z \, dZ \\
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| &= \int_{0}^{\infty} \frac{1}{\pi} e^{-(Y^2+1)u} \, du \quad\quad \text{(let }u=Z^2/2\text{)}\\
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| &= -\frac{1}{\pi(Y^2+1)} e^{-(Y^2+1)u}\Bigg]_{u=0}^{\infty} \\
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| &= \frac{1}{\pi(Y^2+1)}
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| \end{align}
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| </math>
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| This is a standard [[Cauchy distribution]].
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| ==See also==
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| * [[Density estimation]]
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| * [[Likelihood function]]
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| * [[List of probability distributions]]
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| * [[Probability mass function]]
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| * [[Secondary measure]]
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| ==Bibliography==
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| *{{cite book
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| | author = [[Pierre Simon de Laplace]]
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| | year = 1812
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| | title = Analytical Theory of Probability}}
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| :: The first major treatise blending calculus with probability theory, originally in French: ''Théorie Analytique des Probabilités''.
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| *{{cite book
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| | author = [[Andrey Kolmogorov|Andrei Nikolajevich Kolmogorov]]
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| | year = 1950
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| | title = Foundations of the Theory of Probability}}
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| :: The modern measure-theoretic foundation of probability theory; the original German version (''Grundbegriffe der Wahrscheinlichkeitsrechnung'') appeared in 1933.
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| *{{cite book
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| | author = [[Patrick Billingsley]]
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| | title = Probability and Measure
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| | publisher = John Wiley and Sons
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| | location = New York, Toronto, London
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| | year = 1979
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| | isbn = 0-471-00710-2}}
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| *{{cite book
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| | author = David Stirzaker
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| | year = 2003
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| | title = Elementary Probability
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| | isbn = 0-521-42028-8}}
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| :: Chapters 7 to 9 are about continuous variables.
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| {{reflist}}
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| ==External links==
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| * {{Springer
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| |title=Density of a probability distribution
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| |id=D/d031110
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| |first=N.G. |last=Ushakov
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| }}
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| * {{MathWorld|ProbabilityDensityFunction}}
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| {{Theory of probability distributions}}
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| {{DEFAULTSORT:Probability Density Function}}
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| [[Category:Theory of probability distributions]]
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| [[Category:Concepts in physics]]
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