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  2. Unimodality - Wikipedia

    en.wikipedia.org/wiki/Unimodality

    In statistics, a unimodal probability distribution or unimodal distribution is a probability distribution which has a single peak. The term "mode" in this context refers to any peak of the distribution, not just to the strict definition of mode which is usual in statistics. If there is a single mode, the distribution function is called "unimodal".

  3. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    Box plot and probability density function of a normal distribution N(0, σ 2). Geometric visualisation of the mode, median and mean of an arbitrary unimodal probability density function.

  4. Mode (statistics) - Wikipedia

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

    When the probability density function of a continuous distribution has multiple local maxima it is common to refer to all of the local maxima as modes of the distribution, so any peak is a mode. Such a continuous distribution is called multimodal (as opposed to unimodal).

  5. Multimodal distribution - Wikipedia

    en.wikipedia.org/wiki/Multimodal_distribution

    A simple bimodal distribution, in this case a mixture of two normal distributions with the same variance but different means. The figure shows the probability density function (p.d.f.), which is an equally-weighted average of the bell-shaped p.d.f.s of the two normal distributions. If the weights were not equal, the resulting distribution could ...

  6. Shape of a probability distribution - Wikipedia

    en.wikipedia.org/wiki/Shape_of_a_probability...

    The shape of a distribution will fall somewhere in a continuum where a flat distribution might be considered central and where types of departure from this include: mounded (or unimodal), U-shaped, J-shaped, reverse-J shaped and multi-modal. [1] A bimodal distribution would have two high points rather than one. The shape of a distribution is ...

  7. Anderson's theorem - Wikipedia

    en.wikipedia.org/wiki/Anderson's_theorem

    The probability density functions of many well-known probability distributions are p-concave for some p, and hence unimodal. If they are also symmetric (e.g. the Laplace and normal distributions ), then Anderson's theorem applies, in which case

  8. Outline of statistics - Wikipedia

    en.wikipedia.org/wiki/Outline_of_statistics

    Probability distribution. Symmetric probability distribution; Unimodal probability distribution; Conditional probability distribution; Probability density function; Cumulative distribution function; Characteristic function; List of probability distributions

  9. Bell-shaped function - Wikipedia

    en.wikipedia.org/wiki/Bell-shaped_function

    These functions are typically continuous or smooth, asymptotically approach zero for large negative/positive x, and have a single, unimodal maximum at small x. Hence, the integral of a bell-shaped function is typically a sigmoid function. Bell shaped functions are also commonly symmetric. Many common probability distribution functions are bell ...