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In statistics, the mode is the value that appears most often in a set of data values. [1] If X is a discrete random variable, the mode is the value x at which the probability mass function takes its maximum value (i.e., x =argmax x i P( X = x i ) ).
This distribution for a = 0, b = 1 and c = 0.5—the mode (i.e., the peak) is exactly in the middle of the interval—corresponds to the distribution of the mean of two standard uniform variables, that is, the distribution of X = (X 1 + X 2) / 2, where X 1, X 2 are two independent random variables with standard uniform distribution in [0, 1]. [1]
Analytically, when the mode(s) of the posterior density can be given in closed form. This is the case when conjugate priors are used. Via numerical optimization such as the conjugate gradient method or Newton's method. This usually requires first or second derivatives, which have to be evaluated analytically or numerically.
For instance the mode of = [,,,,] is 4. In case of a tie, any of the most frequent elements might be picked as the mode. A range mode query consists in pre-processing [,] such that we can find the mode in any range of [,]. Several data structures have been devised to solve this problem, we summarize some of the results in the following table.
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.
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Comparison of mean, median and mode of two log-normal distributions with different skewness. The mode is the point of global maximum of the probability density function. In particular, by solving the equation ( ln f ) ′ = 0 {\displaystyle (\ln f)'=0} , we get that:
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