When.com Web Search

Search results

  1. Results From The WOW.Com Content Network
  2. Moment (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Moment_(mathematics)

    The fourth central moment is a measure of the heaviness of the tail of the distribution. Since it is the expectation of a fourth power, the fourth central moment, where defined, is always nonnegative; and except for a point distribution, it is always strictly positive. The fourth central moment of a normal distribution is 3σ 4.

  3. Central moment - Wikipedia

    en.wikipedia.org/wiki/Central_moment

    In probability theory and statistics, a central moment is a moment of a probability distribution of a random variable about the random variable's mean; that is, it is the expected value of a specified integer power of the deviation of the random variable from the mean. The various moments form one set of values by which the properties of a ...

  4. Kurtosis - Wikipedia

    en.wikipedia.org/wiki/Kurtosis

    For a sample of n values, a method of moments estimator of the population excess kurtosis can be defined as = = = (¯) [= (¯)] where m 4 is the fourth sample moment about the mean, m 2 is the second sample moment about the mean (that is, the sample variance), x i is the i th value, and ¯ is the sample mean.

  5. Poisson distribution - Wikipedia

    en.wikipedia.org/wiki/Poisson_distribution

    In probability theory and statistics, the Poisson distribution (/ ˈ p w ɑː s ɒ n /) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time if these events occur with a known constant mean rate and independently of the time since the last event. [1]

  6. Method of moments (statistics) - Wikipedia

    en.wikipedia.org/wiki/Method_of_moments_(statistics)

    An example application of the method of moments is to estimate polynomial probability density distributions. In this case, an approximating polynomial of order is defined on an interval [,]. The method of moments then yields a system of equations, whose solution involves the inversion of a Hankel matrix. [2]

  7. Cumulant - Wikipedia

    en.wikipedia.org/wiki/Cumulant

    Any two probability distributions whose moments are identical will have identical cumulants as well, and vice versa. The first cumulant is the mean, the second cumulant is the variance, and the third cumulant is the same as the third central moment. But fourth and higher-order cumulants are not equal to central moments.

  8. Moment measure - Wikipedia

    en.wikipedia.org/wiki/Moment_measure

    In probability and statistics, a moment measure is a mathematical quantity, function or, more precisely, measure that is defined in relation to mathematical objects known as point processes, which are types of stochastic processes often used as mathematical models of physical phenomena representable as randomly positioned points in time, space or both.

  9. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    Comparison of probability density functions, () for the sum of fair 6-sided dice to show their convergence to a normal distribution with increasing , 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).