@article{oai:kyutech.repo.nii.ac.jp:00001513,
 author = {Hirose, Hideo and 廣瀬, 英雄 and Todoroki, Akihiro},
 issue = {6},
 journal = {Information},
 month = {Nov},
 note = {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 the rejection method. A newly developed modified adaptive rejection method works well in the case of log-convex density functions.},
 pages = {829--836},
 title = {Random number generation for the generalized normal distribution using the modified adaptive rejection method},
 volume = {8},
 year = {2005},
 yomi = {ヒロセ, ヒデオ}
}