| When
we want to grasp the characteristics of the time series signals
emitted massively from electric power apparatuses or electroencephalogram,
and want to decide some diagnoses about the apparatuses or human
brains, we may use some statistical distribution functions. In
such cases, the generalized normal distribution is frequently used
in pattern analysts. In assessing the correctness of the estimates
of the shape of the distribution function accurately, we often
use a Monte Carlo simulation study; thus, a fast and efficient
random number generation method for the distribution function is
needed. However, the method for generating the random numbers of
the distribution seems not easy and not yet to have been developed.
In this paper, we propose a random number generation method for
the distribution function using the rejection method. A newly developed
modified adaptive rejection method works well in the case of log-convex
density functions.
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modified adaptive rejection method, exponential
distribution, generalized normal distribution,
envelop function, log-convex, log-concave
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