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  2. Characteristic function (probability theory) - Wikipedia

    en.wikipedia.org/wiki/Characteristic_function...

    The characteristic function of a real-valued random variable always exists, since it is an integral of a bounded continuous function over a space whose measure is finite. A characteristic function is uniformly continuous on the entire space. It is non-vanishing in a region around zero: φ(0) = 1. It is bounded: | φ(t) | ≤ 1.

  3. Probability distribution - Wikipedia

    en.wikipedia.org/wiki/Probability_distribution

    A real-valued discrete random variable can equivalently be defined as a random variable whose cumulative distribution function increases only by jump discontinuities—that is, its cdf increases only where it "jumps" to a higher value, and is constant in intervals without jumps. The points where jumps occur are precisely the values which the ...

  4. Euler characteristic - Wikipedia

    en.wikipedia.org/wiki/Euler_characteristic

    Its Euler characteristic is 0, by the product property. More generally, any compact parallelizable manifold, including any compact Lie group, has Euler characteristic 0. [12] The Euler characteristic of any closed odd-dimensional manifold is also 0. [13] The case for orientable examples is a corollary of Poincaré duality.

  5. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    In this example, the ratio (probability of living during an interval) / (duration of the interval) is approximately constant, and equal to 2 per hour (or 2 hour −1). For example, there is 0.02 probability of dying in the 0.01-hour interval between 5 and 5.01 hours, and (0.02 probability / 0.01 hours) = 2 hour −1.

  6. Poisson distribution - Wikipedia

    en.wikipedia.org/wiki/Poisson_distribution

    In probability theory and statistics, the Poisson distribution (/ ˈ p w ɑː s ɒ n /; French pronunciation:) 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]

  7. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    If the characteristic function of some random variable is of the form () = ⁡ in a neighborhood of zero, where () is a polynomial, then the Marcinkiewicz theorem (named after Józef Marcinkiewicz) asserts that can be at most a quadratic polynomial, and therefore is a normal random variable. [33]

  8. Null hypothesis - Wikipedia

    en.wikipedia.org/wiki/Null_hypothesis

    Those specifying a parameter range or interval. Examples: μ ≤ 100; 95 ≤ μ ≤ 105. Fisher required an exact null hypothesis for testing (see the quotations below). A one-tailed hypothesis (tested using a one-sided test) [2] is an inexact hypothesis in which the value of a parameter is specified as being either: above or equal to a certain ...

  9. Indicator function - Wikipedia

    en.wikipedia.org/wiki/Indicator_function

    In classical mathematics, characteristic functions of sets only take values 1 (members) or 0 (non-members). In fuzzy set theory, characteristic functions are generalized to take value in the real unit interval [0, 1], or more generally, in some algebra or structure (usually required to be at least a poset or lattice).