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Topic: Uniform distribution (continuous)

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Laplace distribution

Exponential distribution

Gamma distribution

Exponential family

Cumulative distribution function

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	Uniform Distribution Theory - journal
		Continuous uniform distribution, discrepancies, distribution of one dimensional and multidimensional sequences.
		Distribution of one dimensional and multidimensional sequences, distribution of binary sequences, spectral properties of sequences, trigonometric sums, dynamic emerging from sequences.
		Distribution of integer sequences and sequences from groups and generalized spaces, theory of distribution functions of sequences (limit measures), distribution of binary sequences, dynamic emerging from sequences.
	udt.mat.savba.sk /udt_editboard.htm (340 words)

	Uniform distribution (continuous) - Wikipedia, the free encyclopedia
		In mathematics, the continuous uniform distributions are probability distributions such that all intervals of the same length are equally probable.
		The continuous uniform distribution is a generalization of the rectangle function because of the shape of its probability density function.
		For n ≥ 2, the nth cumulant of the uniform distribution on the interval [0, 1] is b
	en.wikipedia.org /wiki/Uniform_distribution_(continuous) (841 words)

	Encyclopedia :: encyclopedia : Continuous function (topology) (Site not responding. Last check: )
		In topology and related areas of mathematics a continuous function is a morphism between topological spaces; that is, a mapping which preserves the topological structure.
		A continuous functions between two topological spaces stays continuous if we strengthen the topology of the domain space or weaken the topology of the codomain space.
		In real analysis continuity of functions is commonly defined using the ε-δ definition which builds on the property of the real line being a metric space.
	www.hallencyclopedia.com /Continuous_function_(topology) (836 words)

	PlanetMath: uniform (continuous) random variable
		is a uniform (continuous) random variable with parameters
		"uniform (continuous) random variable" is owned by mathcam.
		This is version 4 of uniform (continuous) random variable, born on 2001-10-26, modified 2004-07-16.
	planetmath.org /encyclopedia/UniformContinousRandomVariable.html (58 words)

	Uniform Distribution (Site not responding. Last check: )
		A uniform distribution is a distribution of a continuous variable in which the probability of X falling within a given interval is proportional to the size of the interval.
		For example, if X is uniformly distributed between 0 and 1, then the probability that X will be between 0.3 and 0.4 is.1, because there are ten intervals of width.1 each.
		However, random number generators often are built to simulate the uniform distribution.
	arnoldkling.com /apstats/uniform.html (167 words)

	Uniform Distribution
		If x is a discrete random variable with a uniform distribution, it attains an integer from a to b inclusive, such that all integers are equally likely.
		If x is a continuous variable with a uniform distribution, it attains all real numbers on the closed interval [a,b] with a uniform probability of 1/(b-a).
		For instance, the distribution function for a continuous uniform variable on [a,b] starts at a,0 and rises linearly to b,1.
	www.mathreference.com /pr,unif.html (730 words)

	Statistics Glossary - random variables and probability distributions
		For a continuous random variable, the cumulative distribution function is the integral of its probability density function.
		The quantile-quantile (Q-Q) plot is constructed using the theoretical cumulative distribution function, F(x), of the specified model.
		The Geometric distribution is related to the Binomial distribution in that both are based on independent trials in which the probability of success is constant and equal to p.
	www.stats.gla.ac.uk /steps/glossary/probability_distributions.html (2101 words)

	Springer Online Reference Works
		A common name for a class of probability distributions, arising as an extension of the idea of "equally possible outcomes" to the continuous case.
		As with the normal distribution, the uniform distribution appears in probability theory as an exact distribution in some problems and as a limit in others.
		The role of the uniform distribution in algebraic groups is played by the normalized Haar measure.
	eom.springer.de /u/u095240.htm (367 words)

	September 27, 2001
		A probability model for X is given by assigning to a set of outcomes A the probability P(A) equal to the area above A and under a curve.
		(#7) The time X (min) for a lab assistant to prepare the equipment for a certain experiment is believed to have a uniform distribution with A=25 and B=35.
		Exercise (#10) A family of pdf's that has been used to approximate the distribution of income, city population size, and size of firms is the Pareto family.
	www.mcs.drexel.edu /~omokliat/courses/math311F02/HANDOUTS/hand17.htm (378 words)

	Boost Random Number Library Distributions
		In addition to the random number generators, this library provides distribution functions which map one distribution (often a uniform distribution provided by some generator) to another.
		The distribution functions no longer satisfy the input iterator requirements (std:24.1.1 [lib.input.iterators]), because this is redundant given the Generator interface and imposes a run-time overhead on all users.
		Such a distribution produces random numbers x > 0 distributed with probability density function p(x) = lambda * exp(-lambda * x), where lambda is the parameter of the distribution.
	www.boost.org /libs/random/random-distributions.html (1426 words)

	Uniform Distribution
		The uniform distribution, also known as a rectangular distribution, is a probability distribution that has constant probability.
		The continuous uniform distribution is a distribution with the probability density function:
		Constructs a uniform distribution with lower lower (a) and upper upper (b).
	freespace.virgin.net /boost.regex/toolkit/html/math_toolkit/dist/dist_ref/dists/uniform_dist.html (492 words)

	ST 352
		A second type of random variable is one that has a continuous distribution. (That is, the random variable is continuous.) We’ll refer to these as continuous random variables.
		Another type of continuous random variable is one that has a normal distribution. Note that a continuous random variable can have many other types of distributions besides a uniform distribution and a normal distribution, but these are the only two we will talk about.
		1. Sketch this distribution. Label the x-axis and identify the mean on the x-axis. Also identify the values on the x-axis one standard deviation greater than and less than the mean.
	oregonstate.edu /instruct/st351/kollath/handouts/normal/contrv.htm (565 words)

	Lecture Notes 6
		There are many continuous probability distributions, such as, uniform distribution, normal distribution, the t distribution, the chi-square distribution, exponential distribution, and F distribution.
		Among the continuous probability distribution, the uniform distribution is the simplest one of all.
		In a uniform distribution, the area under the curve is equal to the product of the length and the height of the rectangle and equals to one.
	business.clayton.edu /arjomand/business/l6.html (2002 words)

	Boost C++ Libraries - Boost Random Number Library Distributions
		In addition to the random number generators, this library provides distribution functions which map one distribution (often a uniform distribution provided by some generator) to another.
		The distribution functions no longer satisfy the input iterator requirements (std:24.1.1 [lib.input.iterators]), because this is redundant given the Generator interface and imposes a run-time overhead on all users.
		Such a distribution produces random numbers x > 0 distributed with probability density function p(x) = lambda * exp(-lambda * x), where lambda is the parameter of the distribution.
	boost.org /libs/random/random-distributions.html (1594 words)

	Cauchy Distribution
		The C(a,b) distribution has a symmetric "bell shaped" probability density function, but it is more peaked at the center and has fatter tails than a normal distribution.
		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.
	www.riskglossary.com /articles/cauchy_distribution.htm (341 words)

	[No title] (Site not responding. Last check: )
		The uniform probability distribution is a discete probability distribution.
		The random variable X that takes on the value of a single dice roll has a uniform distribution, since X can assume any of the 6 values in the set {1, 2, 3, 4, 5, 6} with an equal probability for each.
		A continuous random variable X is said to follow a Uniform distribution with parameters a and b, written X ~ Un(a,b), if its probability density function is constant within a finite interval [a,b], and zero outside this interval,
	www.eng.morgan.edu /~lwalker/normal.html (212 words)

	[No title]
		Some reasons for the popularity of the normal distribution are as follows: (1) The distributions of many random variables, such as the height of a group of students, the length of ears of corns, the errors made in measuring a person's blood pressure, are approximately normally distributed.
		(4) Even if the distribution of the original population is far from normal, the distribution of sample means tends to become normally distributed if the sample sizes are sufficiently large.¡ÁÁóª ¨u The probability density function of a normal random variable with mean m and standard deviation s is Normal distribution has several very good properties.
		Third, the population mean, the population median, and the population mode of a normal random are overlap.
	pegasus.cc.ucf.edu /~wliu/STA2023/chp5.ppt (886 words)

	1.3.6.6.2. Uniform Distribution
		The uniform survival function can be computed from the uniform cumulative distribution function.
		The uniform inverse survival function can be computed from the uniform percent point function.
		One of the most important applications of the uniform distribution is in the generation of random numbers.
	www.itl.nist.gov /div898/handbook/eda/section3/eda3662.htm (364 words)

	Introduction To Monte Carlo Simulation
		Normal/Gaussian Distribution - Continuous distribution applied in situations where the mean and the standard deviation are given and the mean represents the most probable value of the variable.
		This is appropriate for a variable ranging from zero to infinity, with positive skewness and with normally distributed natural logarithm.
		In contrast to the triangular distribution, the likelihood of occurrence of the values between the minimum and maximum is the same.
	www.investopedia.com /articles/07/monte_carlo_intro.asp (397 words)

	Continuous Uniform Distribution
		The uniform distribution describes a variable where the probability of occurrence of any value between in range defined by the minimum and maximum values is equal.
		An important application of the uniform distribution is a form of random number generator which uses a cumulative probability curve to transform a uniform variable to a non-random one.
		A continuous distribution with minimum=0 and maximum=1 is often referred to as a standard uniform distribution.
	www.brighton-webs.co.uk /distributions/uniformc.asp (225 words)

	03/28/00
		Let X be a continuous random variable with pdf f(x) and cdf F(x).
		A continuous random variable X is said to have a normal distribution with parameters m and s, if the pdf of X is
		The normal distribution with parameters m=0 and s=1 is called a standard normal distribution.
	www.mcs.drexel.edu /~omokliat/courses/mcs311S01/HANDOUTS/HAND14.html (593 words)

	Continuous Distributions
		Thus, continuous distributions are in complete contrast with discrete distributions, for which all of the probability mass is concentrated on a discrete set.
		The family of normal distributions is studied in detail in the chapter on Special Distributions.
		Uniform distributions on rectangles in the plane play a fundamental role in Geometric Models.
	www.math.uah.edu /statold/dist/Continuous.xhtml (1553 words)

	Continuous Distributions
		The distribution defined in the last exercise is an example of a beta distribution.
		The distribution defined in the last exercise is the standard normal distribution, perhaps the most important distribution in probability.
		is uniformly distributed on the interval (0, 2
	www.ds.unifi.it /VL/VL_EN/dist/dist2.html (1297 words)

	UNU.RAN - Universal Non-Uniform RANdom number generators
		a non-standard distribution or a truncated distribution is needed.
		Altough there is no need to change these parameters or even know about their existence for "usual distributions", they allow a fine tuning of the generator to work with distributions with some awkward properties.
		Uniform Random Number Generators: All generator objects need one (or more) streams of uniform random numbers that are transformed into random variates of the given distribution.
	statistik.wu-wien.ac.at /unuran (749 words)

	Convergence in Distribution
		have the continuous uniform distribution on the interval
		As Exercise 2 shows, it is quite possible to have a sequence of discrete distributions converge to a continuous distribution (or the other way around).
		Recall that probability density functions have very different meanings in the discrete and continuous cases: density with respect to counting measure in the first case, and density with respect to Lebesgue measure in the second case.
	www.math.uah.edu /statold/dist/Convergence.xhtml (1209 words)

	[No title]
		Well, first of all we need to have at our disposal a source of random (or pseudo-random) numbers with a uniform distribution in [0,1) (which will be the case in the assignment).
		- For example, the cumulative distribution function for a continuous uniform random variable in [a,b] is F_X(x) = (x-a)/(b-a), so we can generate values from this distribution from uniform y in [0,1) by letting x = a + y*(b-a) (which is just what you'd expect).
		If we need a discrete uniform value in [a,b], then we get that we should compute x = a + floor(y * (b-a+1)) (which is again what you'd expect).
	www.cs.toronto.edu /~fpitt/CSC270/1999W/LN09.txt (760 words)

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