qualifying distribution - определение. Что такое qualifying distribution
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Что (кто) такое qualifying distribution - определение

CONTINUOUS PROBABILITY DISTRIBUTION ON THE CIRCLE
Circular normal distribution; Mises distribution; Tikhonov distribution; Normal circular distribution
  • Plot of the von Mises CMF
  • Plot of the von Mises PMF
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Distribution (marketing)         
  • Types of distribution systems
  • The advent of "category killers", such as Australia's Officeworks, has contributed to an increase in channel switching behaviour.
  • [[Harrods]]' food hall, a major retailer in London
  • In an intensive distribution approach, the marketer relies on chain stores to reach broad markets in a cost efficient manner.
  • A wholesale fish market at [[Haikou New Port]], China
MAKING PRODUCTS AVAILABLE TO CUSTOMERS
Distribution (business); Distribution company; Distribution Channels; Distribution channel; Channel captain; Channel (marketing); Channel partners; Distributor (business); Authorized distribution; Product distributor; Multi Channel Distribution; Distribution channels; Distribution chain
Distribution (or place) is one of the four elements of the marketing mix. Distribution is the process of making a product or service available for the consumer or business user who needs it.
Rice distribution         
PROBABILITY DISTRIBUTION OF THE MAGNITUDE OF A CIRCULAR BIVARIATE NORMAL RANDOM VARIABLE
Rician distribution; Ricean distribution
In probability theory, the Rice distribution or Rician distribution (or, less commonly, Ricean distribution) is the probability distribution of the magnitude of a circularly-symmetric bivariate normal random variable, possibly with non-zero mean (noncentral). It was named after Stephen O.
Electric power distribution         
  • arc-lamp]] lighting used outdoors or in large indoor spaces, such as this [[Brush Electric Company]] system installed in 1880 in [[New York City]].
  • electricity networks]]. The voltages and loadings are typical of a European network.
  • Substation near [[Yellowknife]], in the Northwest Territories of Canada
  • 60 Hz}}.
  • High voltage power pole in rural [[Butte County, California]]
  • World map of mains voltage and frequencies
FINAL STAGE OF ELECTRICITY DELIVERY TO INDIVIDUAL CONSUMERS IN A POWER GRID
Distribution grid; Power distribution; Electrical distribution; Electrical power distribution; Electric distribution systems; Electric power distribution grid; Distribution Of Electricity; Distribution of electricity; Electric distribution network; Electricity distribution system; Electrical service; Public electricity network; Electrical distributor; Electrical Distributors; Electrical distributors; Electrical distribution industry; Distribution System Operator (DSO); Distribution system operators; Electrical supply network; Electricity infrastructure; Electric distribution; Electricity distribution; Distribution network; Energy distribution; Electric power distribution system; Distribution feeder; Electrical distribution network; Primary distribution line; Electrical distribution system; Distribution line; Distribution lines
Electric power distribution is the final stage in the delivery of electric power; it carries electricity from the transmission system to individual consumers. Distribution substations connect to the transmission system and lower the transmission voltage to medium voltage ranging between and with the use of transformers.
Ratio distribution         
  • Evaluating the cumulative distribution of a ratio
PROBABILITY DISTRIBUTION CONSTRUCTED AS THE DISTRIBUTION OF THE RATIO OF RANDOM VARIABLES HAVING TWO OTHER KNOWN DISTRIBUTIONS
Quotient distribution; Ratio Distribution; Random ratio; Normal ratio distribution; Ratio distributions; Ratio normal distribution; Complex normal ratio distribution
A ratio distribution (also known as a quotient distribution) is a probability distribution constructed as the distribution of the ratio of random variables having two other known distributions.
Cauchy distribution         
  • date=2018-02-21}}</ref>
  • Fitted cumulative Cauchy distribution to maximum one-day rainfalls using [[CumFreq]], see also [[distribution fitting]]<ref name=cumfreq/>
  • Estimating the mean and standard deviation through samples from a Cauchy distribution (bottom) does not converge with more samples, as in the [[normal distribution]] (top). There can be arbitrarily large jumps in the estimates, as seen in the graphs on the bottom. (Click to expand)
  • Observed histogram and best fitting normal density function.<ref name=cumfreq/>
PROBABILITY DISTRIBUTION
Lorentz distribution; Lorentzian function; Cauchy-Lorentz distribution; Lorentzian lineshape; Lorentzian Lineshape; Cauchy Distribution; Lorentzian distribution; Cauchy Random Variable; Cauchy noise; Lorentzian profile; Lorentz profile; Lorentzian Function; Cauchy–Lorentz distribution; Multivariate Cauchy distribution; Lorentz function; Lorenz distribution; Cauchy random variable
The Cauchy distribution, named after Augustin Cauchy, is a continuous probability distribution. It is also known, especially among physicists, as the Lorentz distribution (after Hendrik Lorentz), Cauchy–Lorentz distribution, Lorentz(ian) function, or Breit–Wigner distribution.
Uniformly         
WIKIMEDIA DISAMBIGUATION PAGE
Uniform distribution (mathematics); Uniformly; Uniformly distributed; Equal distribution; Uniform random variable; Uniform distribution (disambiguation)
·adv In a uniform manner; without variation or diversity; by a regular, constant, or common ratio of change; with even tenor; as, a temper uniformly mild.
uniformly         
WIKIMEDIA DISAMBIGUATION PAGE
Uniform distribution (mathematics); Uniformly; Uniformly distributed; Equal distribution; Uniform random variable; Uniform distribution (disambiguation)
log-normal         
  • mode]] of two log-normal distributions with different [[skewness]].
  • Fitted cumulative log-normal distribution to annually maximum 1-day rainfalls, see [[distribution fitting]]
  • Overview of parameterizations of the log-normal distributions.
  • Relation between normal and log-normal distribution. If <math>Y=\mu+\sigma Z</math> is normally distributed, then <math>X\sim e^{Y}</math> is log-normally distributed.
  • a. <math>y</math> is a log-normal variable with <math>\mu=1, \sigma=0.5</math>. <math>p(\sin y>0)</math> is computed by transforming to the normal variable <math>x = \ln y</math>, then integrating its density over the domain defined by <math>\sin e^x>0</math> (blue regions), using the numerical method of ray-tracing.<ref name="Das" /> b & c. The pdf and cdf of the function <math> \sin y</math> of the log-normal variable can also be computed in this way.
PROBABILITY DISTRIBUTION
Log-normal; Lognormal distribution; Lognormal; Log normal; Log normal distribution; Lognormal Distribution; Log-Normal; Logarithmic normal distribution; Galton distribution; Galton's distribution; Log-normality; Multiplicative Central Limit Theorem; Log-normal distributions
¦ adjective Statistics of or denoting a set of data in which the logarithm of the variate follows a normal distribution.
bell-shaped         
  • [[Carl Friedrich Gauss]] discovered the normal distribution in 1809 as a way to rationalize the [[method of least squares]].
  • As the number of discrete events increases, the function begins to resemble a normal distribution
  • Comparison of probability density functions, <math>p(k)</math> for the sum of <math>n</math> fair 6-sided dice to show their convergence to a normal distribution with increasing <math>na</math>, in accordance to the central limit theorem. In the bottom-right graph, smoothed profiles of the previous graphs are rescaled, superimposed and compared with a normal distribution (black curve).
  • Histogram of sepal widths for ''Iris versicolor'' from Fisher's [[Iris flower data set]], with superimposed best-fitting normal distribution.
  • Fitted cumulative normal distribution to October rainfalls, see [[distribution fitting]]
  •  [[Pierre-Simon Laplace]] proved the [[central limit theorem]] in 1810, consolidating the importance of the normal distribution in statistics.
  • The [[bean machine]], a device invented by [[Francis Galton]], can be called the first generator of normal random variables. This machine consists of a vertical board with interleaved rows of pins. Small balls are dropped from the top and then bounce randomly left or right as they hit the pins. The balls are collected into bins at the bottom and settle down into a pattern resembling the Gaussian curve.
  • '''a:''' Probability density of a function <math>\cos x^2</math> of a normal variable <math>x</math> with <math>\mu=-2</math> and <math>\sigma=3</math>. '''b:''' Probability density of a function <math>x^y</math> of two normal variables <math>x</math> and <math>y</math>, where <math>\mu_x=1</math>, <math>\mu_y=2</math>, <math>\sigma_x = 0.1</math>, <math>\sigma_y = 0.2</math>, and <math>\rho_{xy} = 0.8</math>. '''c:''' Heat map of the joint probability density of two functions of two correlated normal variables <math>x</math> and <math>y</math>, where <math>\mu_x = -2</math>, <math>\mu_y=5</math>, <math>\sigma_x^2 = 10</math>, <math>\sigma_y^2 = 20</math>, and <math>\rho_{xy} = 0.495</math>. '''d:''' Probability density of a function <math display="inline">\sum_{i=1}^4 \vert x_i \vert</math> of 4 iid standard normal variables. These are computed by the numerical method of ray-tracing.<ref name="Das" />
  • The ground state of a [[quantum harmonic oscillator]] has the [[Gaussian distribution]].
  • For the normal distribution, the values less than one standard deviation away from the mean account for 68.27% of the set; while two standard deviations from the mean account for 95.45%; and three standard deviations account for 99.73%.
PROBABILITY DISTRIBUTION
Bell Curve; Gaussian distribution; NormalDistribution; Normal Distribution; Standard normal distribution; Law of error; Cumulative normal; Normally distributed; Cumulative Normal distribution; Normality (statistics); Standard normal; Normal density function; Normal curve; Normal distribution curve; Normal Curve; Normal random variable; The bell-shaped curve; Gaussian normal distribution; Gaussian Distributions; Gaussian Distribution; Bell-shaped; Gaussian random variable; Error Distribution; Bell-shaped curve; Standard distribution; Error distribution; Bell-curve; Normal distributions; Bell distribution; Normal probability distribution; Gaussian density; Gauss distribution; Normal cumulative distribution function; Bell Curves; Bell curves; Normal distribution about the mean; Gaussian probability density function; Gaussian probability distribution; Normal Model; Standard normal random variable; Gaussian profile; Normal-distribution; Bell-shaped frequency distribution curve; Gaussian distributions; Normal distribution quantile function; E-x2; E−x2; Normal population; Cumulative distribution function of the normal distribution; Bellcurve; Univariate Gaussian; Univariate Gaussian distribution; Bell curve; Bell shaped curve; Operations on normal deviates; Operations on normal distributions; Normal deviate; Standard normally distributed; Approximately normal distribution; Normalcdf; Gaussian pdf; Normal density; Normaldist
a.
(Bot.) Campanulate.
bell curve         
  • [[Carl Friedrich Gauss]] discovered the normal distribution in 1809 as a way to rationalize the [[method of least squares]].
  • As the number of discrete events increases, the function begins to resemble a normal distribution
  • Comparison of probability density functions, <math>p(k)</math> for the sum of <math>n</math> fair 6-sided dice to show their convergence to a normal distribution with increasing <math>na</math>, in accordance to the central limit theorem. In the bottom-right graph, smoothed profiles of the previous graphs are rescaled, superimposed and compared with a normal distribution (black curve).
  • Histogram of sepal widths for ''Iris versicolor'' from Fisher's [[Iris flower data set]], with superimposed best-fitting normal distribution.
  • Fitted cumulative normal distribution to October rainfalls, see [[distribution fitting]]
  •  [[Pierre-Simon Laplace]] proved the [[central limit theorem]] in 1810, consolidating the importance of the normal distribution in statistics.
  • The [[bean machine]], a device invented by [[Francis Galton]], can be called the first generator of normal random variables. This machine consists of a vertical board with interleaved rows of pins. Small balls are dropped from the top and then bounce randomly left or right as they hit the pins. The balls are collected into bins at the bottom and settle down into a pattern resembling the Gaussian curve.
  • '''a:''' Probability density of a function <math>\cos x^2</math> of a normal variable <math>x</math> with <math>\mu=-2</math> and <math>\sigma=3</math>. '''b:''' Probability density of a function <math>x^y</math> of two normal variables <math>x</math> and <math>y</math>, where <math>\mu_x=1</math>, <math>\mu_y=2</math>, <math>\sigma_x = 0.1</math>, <math>\sigma_y = 0.2</math>, and <math>\rho_{xy} = 0.8</math>. '''c:''' Heat map of the joint probability density of two functions of two correlated normal variables <math>x</math> and <math>y</math>, where <math>\mu_x = -2</math>, <math>\mu_y=5</math>, <math>\sigma_x^2 = 10</math>, <math>\sigma_y^2 = 20</math>, and <math>\rho_{xy} = 0.495</math>. '''d:''' Probability density of a function <math display="inline">\sum_{i=1}^4 \vert x_i \vert</math> of 4 iid standard normal variables. These are computed by the numerical method of ray-tracing.<ref name="Das" />
  • The ground state of a [[quantum harmonic oscillator]] has the [[Gaussian distribution]].
  • For the normal distribution, the values less than one standard deviation away from the mean account for 68.27% of the set; while two standard deviations from the mean account for 95.45%; and three standard deviations account for 99.73%.
PROBABILITY DISTRIBUTION
Bell Curve; Gaussian distribution; NormalDistribution; Normal Distribution; Standard normal distribution; Law of error; Cumulative normal; Normally distributed; Cumulative Normal distribution; Normality (statistics); Standard normal; Normal density function; Normal curve; Normal distribution curve; Normal Curve; Normal random variable; The bell-shaped curve; Gaussian normal distribution; Gaussian Distributions; Gaussian Distribution; Bell-shaped; Gaussian random variable; Error Distribution; Bell-shaped curve; Standard distribution; Error distribution; Bell-curve; Normal distributions; Bell distribution; Normal probability distribution; Gaussian density; Gauss distribution; Normal cumulative distribution function; Bell Curves; Bell curves; Normal distribution about the mean; Gaussian probability density function; Gaussian probability distribution; Normal Model; Standard normal random variable; Gaussian profile; Normal-distribution; Bell-shaped frequency distribution curve; Gaussian distributions; Normal distribution quantile function; E-x2; E−x2; Normal population; Cumulative distribution function of the normal distribution; Bellcurve; Univariate Gaussian; Univariate Gaussian distribution; Bell curve; Bell shaped curve; Operations on normal deviates; Operations on normal distributions; Normal deviate; Standard normally distributed; Approximately normal distribution; Normalcdf; Gaussian pdf; Normal density; Normaldist
¦ noun Mathematics a graph of a normal (Gaussian) distribution, with a large rounded peak tapering away at each end.

Википедия

Von Mises distribution

In probability theory and directional statistics, the von Mises distribution (also known as the circular normal distribution or Tikhonov distribution) is a continuous probability distribution on the circle. It is a close approximation to the wrapped normal distribution, which is the circular analogue of the normal distribution. A freely diffusing angle θ {\displaystyle \theta } on a circle is a wrapped normally distributed random variable with an unwrapped variance that grows linearly in time. On the other hand, the von Mises distribution is the stationary distribution of a drift and diffusion process on the circle in a harmonic potential, i.e. with a preferred orientation. The von Mises distribution is the maximum entropy distribution for circular data when the real and imaginary parts of the first circular moment are specified. The von Mises distribution is a special case of the von Mises–Fisher distribution on the N-dimensional sphere.