{"title":"Optimisation of Algorithms Generating Pseudorandom Integers with Binomial Distribution","authors":"R. Horváth","doi":"10.1109/ICETA57911.2022.9974736","DOIUrl":null,"url":null,"abstract":"This article summarises the creation of two approaches how to produce pseudorandom integers with binomial distribution and their comparison with other selected implementations. The first approach does the work by preparing a probability table searchable by the binary search algorithm, and the second one is about generating the values using the Galton board simulation. The second approach is slower (significantly slower for bigger numbers of trials) but is applicable in situations where the probability of success (connected to the binomial distribution process) is not uniformly distributed.","PeriodicalId":151344,"journal":{"name":"2022 20th International Conference on Emerging eLearning Technologies and Applications (ICETA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 20th International Conference on Emerging eLearning Technologies and Applications (ICETA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETA57911.2022.9974736","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This article summarises the creation of two approaches how to produce pseudorandom integers with binomial distribution and their comparison with other selected implementations. The first approach does the work by preparing a probability table searchable by the binary search algorithm, and the second one is about generating the values using the Galton board simulation. The second approach is slower (significantly slower for bigger numbers of trials) but is applicable in situations where the probability of success (connected to the binomial distribution process) is not uniformly distributed.