{"title":"贝叶斯统计方法对象棋引擎的优化","authors":"Ivan Ivec, Ivana Vojnovi'c","doi":"10.32817/ams.2.5","DOIUrl":null,"url":null,"abstract":"We develop a new method for stochastic optimization using the Bayesian statistics approach. More precisely, we optimize parameters of chess engines as those data are available to us, but the method should apply to all situations where we want to optimize a certain gain/loss function which has no analytical form and thus cannot be measured directly but only by comparison of two parameter sets. We also experimentally compare the new method with the famous SPSA method.","PeriodicalId":309225,"journal":{"name":"Acta mathematica Spalatensia","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian statistics approach to chess engines optimization\",\"authors\":\"Ivan Ivec, Ivana Vojnovi'c\",\"doi\":\"10.32817/ams.2.5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop a new method for stochastic optimization using the Bayesian statistics approach. More precisely, we optimize parameters of chess engines as those data are available to us, but the method should apply to all situations where we want to optimize a certain gain/loss function which has no analytical form and thus cannot be measured directly but only by comparison of two parameter sets. We also experimentally compare the new method with the famous SPSA method.\",\"PeriodicalId\":309225,\"journal\":{\"name\":\"Acta mathematica Spalatensia\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta mathematica Spalatensia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32817/ams.2.5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta mathematica Spalatensia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32817/ams.2.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian statistics approach to chess engines optimization
We develop a new method for stochastic optimization using the Bayesian statistics approach. More precisely, we optimize parameters of chess engines as those data are available to us, but the method should apply to all situations where we want to optimize a certain gain/loss function which has no analytical form and thus cannot be measured directly but only by comparison of two parameter sets. We also experimentally compare the new method with the famous SPSA method.
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