Note that under this definition the uniform distribution is unimodal, as well as any other distribution in which the maximum distribution is achieved for a range of values, e.g. If the cdf is convex for x m, then the distribution is unimodal, m being the mode. In continuous distributions, unimodality can be defined through the behavior of the cumulative distribution function (cdf). Other definitions of unimodality in distribution functions also exist. Among discrete distributions, the binomial distribution and Poisson distribution can be seen as unimodal, though for some parameters they can have two adjacent values with the same probability.įigure 2 and Figure 3 illustrate bimodal distributions. Other examples of unimodal distributions include Cauchy distribution, Student's t-distribution, chi-squared distribution and exponential distribution. Figure 1 illustrates normal distributions, which are unimodal. If it has more modes it is "bimodal" (2), "trimodal" (3), etc., or in general, "multimodal". If there is a single mode, the distribution function is called "unimodal". 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. In statistics, a unimodal probability distribution or unimodal distribution is a probability distribution which has a single peak. Note that only the largest peak would correspond to a mode in the strict sense of the definition of mode
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