In
probability theory and
statistics, the continuous uniform distributions or rectangular distributions are a family of
symmetricprobability distributions. Such a distribution describes an experiment where there is an arbitrary outcome that lies between certain bounds.[1] The bounds are defined by the parameters, and which are the minimum and maximum values. The interval can either be
closed (i.e. ) or
open (i.e. ).[2] Therefore, the distribution is often abbreviated where stands for uniform distribution.[1] The difference between the bounds defines the interval length; all
intervals of the same length on the distribution's
support are equally probable. It is the
maximum entropy probability distribution for a
random variable under no constraint other than that it is contained in the distribution's support.[3]
The values of at the two boundaries and are usually unimportant, because they do not alter the value of over any interval nor of nor of any higher moment. Sometimes they are chosen to be zero, and sometimes chosen to be The latter is appropriate in the context of estimation by the method of
maximum likelihood. In the context of
Fourier analysis, one may take the value of or to be because then the inverse transform of many
integral transforms of this uniform function will yield back the function itself, rather than a function which is equal "
almost everywhere", i.e. except on a set of points with zero
measure. Also, it is consistent with the
sign function, which has no such ambiguity.
Any probability density function integrates to so the probability density function of the continuous uniform distribution is graphically portrayed as a rectangle where is the base length and is the height. As the base length increases, the height (the density at any particular value within the distribution boundaries) decreases.[4]
In terms of mean and variance the probability density function of the continuous uniform distribution is
In a graphical representation of the continuous uniform distribution function the area under the curve within the specified bounds, displaying the probability, is a rectangle. For the specific example above, the base would be and the height would be [5]
Example 2. Using the continuous uniform distribution function (conditional)
For a random variable find
The example above is a
conditional probability case for the continuous uniform distribution: given that is true, what is the probability that Conditional probability changes the sample space, so a new interval length has to be calculated, where and [5] The graphical representation would still follow Example 1, where the area under the curve within the specified bounds displays the probability; the base of the rectangle would be and the height would be [5]
For a random variable following the continuous uniform distribution, the
expected value is and the
variance is
For the special case the probability density function of the continuous uniform distribution is:
the moment-generating function reduces to the simple form:
Cumulant-generating function
For the -th
cumulant of the continuous uniform distribution on the interval is where is the -th
Bernoulli number.[8]
Standard uniform distribution
The continuous uniform distribution with parameters and i.e. is called the standard uniform distribution.
One interesting property of the standard uniform distribution is that if has a standard uniform distribution, then so does This property can be used for generating
antithetic variates, among other things. In other words, this property is known as the
inversion method where the continuous standard uniform distribution can be used to generate
random numbers for any other continuous distribution.[4] If is a uniform random number with standard uniform distribution, i.e. with then generates a random number from any continuous distribution with the specified
cumulative distribution function[4]
Relationship to other functions
As long as the same conventions are followed at the transition points, the probability density function of the continuous uniform distribution may also be expressed in terms of the
Heaviside step function as:
There is no ambiguity at the transition point of the
sign function. Using the half-maximum convention at the transition points, the continuous uniform distribution may be expressed in terms of the sign function as:
Properties
Moments
The mean (first raw
moment) of the continuous uniform distribution is:
The second raw moment of this distribution is:
In general, the -th raw moment of this distribution is:
The variance (second
central moment) of this distribution is:
The probability that a continuously uniformly distributed random variable falls within any interval of fixed length is independent of the location of the interval itself (but it is dependent on the interval size ), so long as the interval is contained in the distribution's support.
Indeed, if and if is a subinterval of with fixed then:
which is independent of This fact motivates the distribution's name.
Generalization to Borel sets
This distribution can be generalized to more complicated sets than intervals. Let be a
Borel set of positive, finite
Lebesgue measure i.e. The uniform distribution on can be specified by defining the probability density function to be zero outside and constantly equal to on
The standard uniform distribution is a special case of the
beta distribution, with parameters (1,1).
The sum of two independent uniform distributions U1(a,b)+U2(c,d) yields a
trapezoidal distribution, symmetric about its mean, on the support [a+c,b+d]. The plateau has width equals to the absolute different of the width of U1 and U2. The width of the sloped parts corresponds to the width of the narrowest uniform distribution.
If the uniform distributions have the same width w, the result is a
triangular distribution, symmetric about its mean, on the support [a+c,a+c+2w].
The sum of two independent, equally distributed, uniform distributions U1(a,b)+U2(a,b) yields a symmetric
triangular distribution on the support [2a,2b].
The distance between two
i.i.d. uniform random variables |U1(a,b)-U2(a,b)| also has a
triangular distribution, although not symmetric, on the support [0,b-a].
The midpoint of the distribution, is both the mean and the median of the uniform distribution. Although both the sample mean and the sample median are
unbiased estimators of the midpoint, neither is as
efficient as the sample
mid-range, i.e. the arithmetic mean of the sample maximum and the sample minimum, which is the
UMVU estimator of the midpoint (and also the
maximum likelihood estimate).
Confidence interval
For the maximum
Let be a sample from where is the maximum value in the population. Then has the Lebesgue-Borel-density [10]
The confidence interval given before is mathematically incorrect, as
cannot be solved for without knowledge of . However, one can solve
for for any unknown but valid
one then chooses the smallest possible satisfying the condition above. Note that the interval length depends upon the random variable
Occurrence and applications
The probabilities for uniform distribution function are simple to calculate due to the simplicity of the function form.[2] Therefore, there are various applications that this distribution can be used for as shown below: hypothesis testing situations, random sampling cases, finance, etc. Furthermore, generally, experiments of physical origin follow a uniform distribution (e.g. emission of radioactive
particles).[1] However, it is important to note that in any application, there is the unchanging assumption that the probability of falling in an interval of fixed length is constant.[2]
Economics example for uniform distribution
In the field of economics, usually
demand and
replenishment may not follow the expected normal distribution. As a result, other distribution models are used to better predict probabilities and trends such as
Bernoulli process.[11] But according to Wanke (2008), in the particular case of investigating
lead-time for inventory management at the beginning of the
life cycle when a completely new product is being analyzed, the uniform distribution proves to be more useful.[11] In this situation, other distribution may not be viable since there is no existing data on the new product or that the demand history is unavailable so there isn't really an appropriate or known distribution.[11] The uniform distribution would be ideal in this situation since the random variable of lead-time (related to demand) is unknown for the new product but the results are likely to range between a plausible range of two values.[11] The
lead-time would thus represent the random variable. From the uniform distribution model, other factors related to
lead-time were able to be calculated such as
cycle service level and
shortage per cycle. It was also noted that the uniform distribution was also used due to the simplicity of the calculations.[11]
The uniform distribution is useful for sampling from arbitrary distributions. A general method is the inverse transform sampling method, which uses the
cumulative distribution function (CDF) of the target random variable. This method is very useful in theoretical work. Since simulations using this method require inverting the CDF of the target variable, alternative methods have been devised for the cases where the CDF is not known in closed form. One such method is
rejection sampling.
In analog-to-digital conversion, a quantization error occurs. This error is either due to rounding or truncation. When the original signal is much larger than one
least significant bit (LSB), the quantization error is not significantly correlated with the signal, and has an approximately uniform distribution. The
RMS error therefore follows from the variance of this distribution.
Random variate generation
There are many applications in which it is useful to run simulation experiments. Many
programming languages come with implementations to generate
pseudo-random numbers which are effectively distributed according to the standard uniform distribution.
If is a value sampled from the standard uniform distribution, then the value follows the uniform distribution parameterized by and as described above.
History
While the historical origins in the conception of uniform distribution are inconclusive, it is speculated that the term "uniform" arose from the concept of
equiprobability in dice games (note that the dice games would have
discrete and not continuous uniform sample space).
Equiprobability was mentioned in
Gerolamo Cardano's Liber de Ludo Aleae, a manual written in 16th century and detailed on advanced probability calculus in relation to dice.[12]
Irwin–Hall distribution — In the degenerate case where n=1, the Irwin-Hall distribution generates a uniform distribution between 0 and 1.
Bates distribution — Similar to the Irwin-Hall distribution, but rescaled for n. Like the Irwin-Hall distribution, in the degenerate case where n=1, the Bates distribution generates a uniform distribution between 0 and 1.
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abcWalpole, Ronald; et al. (2012). Probability & Statistics for Engineers and Scientists. Boston, USA: Prentice Hall. pp. 171–172.
ISBN978-0-321-62911-1.