{"title":"书评:;精算学习的统计基础及其应用","authors":"Olivier Menoukeu-Pamen","doi":"10.1137/24m1651575","DOIUrl":null,"url":null,"abstract":"SIAM Review, Volume 67, Issue 3, Page 656-658, August 2025. <br/> In insurance mathematics and actuarial sciences, modeling the dynamics of insured events is a pivotal challenge that demands advanced and sophisticated techniques due to the growing complexity of insurance markets. This complexity, coupled with the exponential growth in data availability in recent years, has acted as a catalyst for the adoption of datacentric approaches in forecasting random phenomena.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"27 1","pages":""},"PeriodicalIF":6.1000,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Book Review:; Statistical Foundations of Actuarial Learning and Its Applications\",\"authors\":\"Olivier Menoukeu-Pamen\",\"doi\":\"10.1137/24m1651575\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"SIAM Review, Volume 67, Issue 3, Page 656-658, August 2025. <br/> In insurance mathematics and actuarial sciences, modeling the dynamics of insured events is a pivotal challenge that demands advanced and sophisticated techniques due to the growing complexity of insurance markets. This complexity, coupled with the exponential growth in data availability in recent years, has acted as a catalyst for the adoption of datacentric approaches in forecasting random phenomena.\",\"PeriodicalId\":49525,\"journal\":{\"name\":\"SIAM Review\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2025-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SIAM Review\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1137/24m1651575\",\"RegionNum\":1,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM Review","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1137/24m1651575","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
Book Review:; Statistical Foundations of Actuarial Learning and Its Applications
SIAM Review, Volume 67, Issue 3, Page 656-658, August 2025. In insurance mathematics and actuarial sciences, modeling the dynamics of insured events is a pivotal challenge that demands advanced and sophisticated techniques due to the growing complexity of insurance markets. This complexity, coupled with the exponential growth in data availability in recent years, has acted as a catalyst for the adoption of datacentric approaches in forecasting random phenomena.
期刊介绍:
Survey and Review feature papers that provide an integrative and current viewpoint on important topics in applied or computational mathematics and scientific computing. These papers aim to offer a comprehensive perspective on the subject matter.
Research Spotlights publish concise research papers in applied and computational mathematics that are of interest to a wide range of readers in SIAM Review. The papers in this section present innovative ideas that are clearly explained and motivated. They stand out from regular publications in specific SIAM journals due to their accessibility and potential for widespread and long-lasting influence.