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Encyclopedia > Cauchy distribution
Cauchy-Lorentz
Probability density function
Probability density function for the Cauchy distribtion
The green line is the standard Cauchy distribution
Cumulative distribution function
Cumulative distribution function for the Normal distribution
Colors match the pdf above
Parameters location (real)
scale (real)
Support
pdf
cdf
Mean (not defined)
Median x0
Mode x0
Variance (not defined)
Skewness (not defined)
Kurtosis (not defined)
Entropy
mgf (not defined)
Char. func.

The Cauchy-Lorentz distribution, named after Augustin Cauchy, is a continuous probability distribution with probability density function Download high resolution version (1300x975, 154 KB) Wikipedia does not have an article with this exact name. ... Image File history File links Download high resolution version (1300x975, 158 KB) Cumulative distribution function for the Cauchy distribution gnuplot source under GPL: dcauchy(x,l,s) = 1. ... In mathematics, the real numbers are intuitively defined as numbers that are in one-to-one correspondence with the points on an infinite line—the number line. ... In statistics, if a family of probabiblity densities parametrized by a parameter s is of the form fs(x) = f(sx)/s then s is called a scale parameter, since its value determines the scale of the probability distribution. ... In mathematics, the support of a numerical function f on a set X is sometimes defined as the subset of X on which f is nonzero. ... In mathematics, a probability density function (pdf) serves to represent a probability distribution in terms of integrals. ... In probability theory, the cumulative distribution function (abbreviated cdf) completely describes the probability distribution of a real-valued random variable, X. For every real number x, the cdf is given by where the right-hand side represents the probability that the variable X takes on a value less than or... In probability theory (and especially gambling), the expected value (or mathematical expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff (value). Thus, it represents the average amount one expects to win per bet if bets with identical... In probability theory and statistics, the median is a number that separates the higher half of a sample, a population, or a probability distribution from the lower half. ... In statistics, the mode is the value that has the largest number of observations, namely the most frequent value or values. ... In probability theory and statistics, the variance of a random variable is a measure of its statistical dispersion, indicating how far from the expected value its values typically are. ... In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable. ... In probability theory and statistics, kurtosis is a measure of the peakedness of the probability distribution of a real-valued random variable. ... Entropy of a Bernoulli trial as a function of success probability. ... In probability theory and statistics, the moment-generating function of a random variable X is The moment-generating function generates the moments of the probability distribution, as follows: If X has a continuous probability density function f(x) then the moment generating function is given by where is the ith... Some mathematicians use the phrase characteristic function synonymously with indicator function. ... Augustin Louis Cauchy Augustin Louis Cauchy (August 21, 1789 – May 23, 1857) was a French mathematician. ... In mathematics, a probability distribution assigns to every interval of the real numbers a probability, so that the probability axioms are satisfied. ... In mathematics, a probability density function (pdf) serves to represent a probability distribution in terms of integrals. ...

where x0 is the location parameter, specifying the location of the peak of the distribution, and γ is the scale parameter which specifies the half-width at half-maximum (HWHM). As a probability distribution, it is known as the Cauchy distribution while among physicists it is known as the Lorentz distribution or the Breit-Wigner distribution. Its importance in physics is largely due to the fact that it is the solution to the differential equation describing forced resonance. In spectroscopy it is the description of the line shape of spectral lines which are broadened by many mechanisms including resonance broadening. The statistical term Cauchy distribution will be used in the following discussion. In statistics, if a family of probabiblity densities parametrized by a scalar- or vector-valued parameter μ is of the form fμ(x) = f(x − μ) then μ is called a location parameter, since its value determines the location of the probability distribution. ... In statistics, if a family of probabiblity densities parametrized by a parameter s is of the form fs(x) = f(sx)/s then s is called a scale parameter, since its value determines the scale of the probability distribution. ... The willingness to question previously held truths and search for new answers resulted in a period of major scientific advancements, now known as the Scientific Revolution. ... In mathematics, a differential equation is an equation in which the derivatives of a function appear as variables. ... In physics, resonance is the tendency of a system to absorb more oscillatory energy when the frequency of the oscillations matches the systems natural frequency of vibration (its resonant frequency) than it does at other frequencies. ... Spectroscopy is the study of spectra, that is, the dependence of physical quantities on frequency. ...


The special case when x0 = 0 and γ = 1 is called the standard Cauchy distribution with the probability density function

Contents


Properties

Since it is a distribution function, it integrates to unity: In calculus, the integral of a function is a generalization of area, mass, volume, sum, and total. ...

The cumulative distribution function is:

and the inverse cumulative distribution function of the Cauchy distribution is

The Cauchy distribution is often cited as an example of a distribution which has no mean, variance or higher moments defined, although its mode and median are well defined and are both equal to x0. In statistics, mean has two related meanings: the average in ordinary English, which is more correctly called the arithmetic mean, to distinguish it from geometric mean or harmonic mean. ... In probability theory and statistics, the variance of a random variable is a measure of its statistical dispersion, indicating how far from the expected value its values typically are. ... See also moment (physics). ... In statistics, the mode is the value that has the largest number of observations, namely the most frequent value or values. ... In probability theory and statistics, the median is a number that separates the higher half of a sample, a population, or a probability distribution from the lower half. ...


The characteristic function of the Cauchy distribution is well defined: Some mathematicians use the phrase characteristic function synonymously with indicator function. ...

When U and V are two independent normally distributed random variables with expected value 0 and variance 1, then the ratio U/V has the standard Cauchy distribution. The normal distribution, also called Gaussian distribution, is an extremely important probability distribution in many fields, especially in physics and engineering. ... A random variable can be thought of as the numeric result of operating a non-deterministic mechanism or performing a non-deterministic experiment to generate a random result. ... In probability theory (and especially gambling), the expected value (or mathematical expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff (value). Thus, it represents the average amount one expects to win per bet if bets with identical... In probability theory and statistics, the variance of a random variable is a measure of its statistical dispersion, indicating how far from the expected value its values typically are. ...


If X1, …, Xn are independent and identically distributed random variables, each with a standard Cauchy distribution, then the sample mean (X1 + … + Xn)/n has the same standard Cauchy distribution. To see that this is true, compute the characteristic function of the sample mean: In probability theory, a sequence or other collection of random variables is independent and identically distributed (i. ... In mathematics and statistics, the arithmetic mean of a set of numbers is the sum of all the members of the set divided by the number of items in the set. ... Some mathematicians use the phrase characteristic function synonymously with indicator function. ...

where is the sample mean. This example serves to show that the hypothesis of finite variance in the central limit theorem cannot be dropped. It is also an example of a more generalized version of the central limit theorem that is characteristic of all Lévy distributions, of which the Cauchy distribution is a special case. Central limit theorem - Wikipedia, the free encyclopedia /**/ @import /skins-1. ... ...


The Cauchy distribution is an infinitely divisible probability distribution. The concept of infinite divisibility arises in different ways in philosophy, physics, economics, order theory (a branch of mathematics), and probability theory (also a branch of mathematics). ...


The standard Cauchy distribution coincides with the Student's t-distribution with one degree of freedom. In probability and statistics, the t-distribution or Students distribution is a probability distribution that arises in the problem of estimating the mean of a normally distributed population when the sample size is small. ...


Why the mean of the Cauchy distribution is undefined

If a probability distribution has a density function f(x) then the mean or expected value is In mathematics, a probability distribution assigns to every interval of the real numbers a probability, so that the probability axioms are satisfied. ... In mathematics, a probability density function (pdf) serves to represent a probability distribution in terms of integrals. ... In probability theory (and especially gambling), the expected value (or mathematical expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff (value). Thus, it represents the average amount one expects to win per bet if bets with identical...

The question is now whether this is the same thing as

If both the positive and negative terms in (2) are finite, then (1) is the same as (2). If either the positive term or the negative term is finite, then (1) is the same as (2) (and is infinite, with either a positive or a negative sign). But in the case of the Cauchy distribution, both are infinite. This means (2) is undefined.


Moreover, if (1) is construed as a Lebesgue integral, then (1) is also undefined, since (1) is then defined simply as the difference (2) between positive and negative parts. In mathematics, the integral of a function of one real variable can be regarded as the area of a plane region bounded by the graph of that function. ...


However, if (1) is construed as an improper integral rather than a Lebesgue integral, then (2) is undefined, and (1) is not necessarily well-defined. We may take (1) to mean It is recommended that the reader be familiar with antiderivatives, integrals, and limits. ...

and this is its Cauchy principal value, which is zero, but we could also take (1) to mean, for example, In mathematics, the Cauchy principal value of certain improper integrals is defined as either the finite number where b is a point at which the behavior of the function f is such that for any a < b and for any c > b (one sign is + and the other is −). or...

which is not zero, as can be seen easily by computing the integral.


Various results in probability theory about expected values, such as the strong law of large numbers, will not work in such cases. In a statistical context, laws of large numbers imply that the average of a random sample from a large population is likely to be close to the mean of the whole population. ...


Why the second moment of the Cauchy distribution is infinite

Without a defined mean, it is impossible to consider the variance or standard deviation of a standard Cauchy distribution. But the second moment about zero can be considered. It turns out to be infinite: In probability theory and statistics, the variance of a random variable is a measure of its statistical dispersion, indicating how far from the expected value its values typically are. ... In probability and statistics, the standard deviation is the most commonly used measure of statistical dispersion. ... Infinity is a word carrying a number of different meanings in mathematics, philosophy, theology and everyday life. ...

External links


  Results from FactBites:
 
1.3.6.6.3. Cauchy Distribution (401 words)
The likelihood functions for the Cauchy maximum likelihood estimates are given in chapter 16 of Johnson, Kotz, and Balakrishnan.
The Cauchy distribution is important as an example of a pathological case.
The mean and standard deviation of the Cauchy distribution are undefined.
Cauchy Distribution (341 words)
The Cauchy distribution is specified with two parameters: a and b.
The Cauchy distribution is a stable Paretian distribution, so a sum of Cauchy random variables is itself Cauchy.
The standard Cauchy distribution is a special case of the student t distribution with one degree of freedom.
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