Pub Date : 2023-09-13DOI: 10.1080/00401706.2023.2257765
John C. Yannotty, Thomas J. Santner, Richard J. Furnstahl, Matthew T. Pratola
In modern computer experiment applications, one often encounters the situation where various models of a physical system are considered, each implemented as a simulator on a computer. An important question in such a setting is determining the best simulator, or the best combination of simulators, to use for prediction and inference. Bayesian model averaging (BMA) and stacking are two statistical approaches used to account for model uncertainty by aggregating a set of predictions through a simple linear combination or weighted average. Bayesian model mixing (BMM) extends these ideas to capture the localized behavior of each simulator by defining input-dependent weights. One possibility is to define the relationship between inputs and the weight functions using a flexible non-parametric model that learns the local strengths and weaknesses of each simulator. This paper proposes a BMM model based on Bayesian Additive Regression Trees (BART). The proposed methodology is applied to combine predictions from Effective Field Theories (EFTs) associated with a motivating nuclear physics application. Supplementary Material is available online. Source code is available at https://github.com/jcyannotty/OpenBT.
{"title":"Model Mixing Using Bayesian Additive Regression Trees","authors":"John C. Yannotty, Thomas J. Santner, Richard J. Furnstahl, Matthew T. Pratola","doi":"10.1080/00401706.2023.2257765","DOIUrl":"https://doi.org/10.1080/00401706.2023.2257765","url":null,"abstract":"In modern computer experiment applications, one often encounters the situation where various models of a physical system are considered, each implemented as a simulator on a computer. An important question in such a setting is determining the best simulator, or the best combination of simulators, to use for prediction and inference. Bayesian model averaging (BMA) and stacking are two statistical approaches used to account for model uncertainty by aggregating a set of predictions through a simple linear combination or weighted average. Bayesian model mixing (BMM) extends these ideas to capture the localized behavior of each simulator by defining input-dependent weights. One possibility is to define the relationship between inputs and the weight functions using a flexible non-parametric model that learns the local strengths and weaknesses of each simulator. This paper proposes a BMM model based on Bayesian Additive Regression Trees (BART). The proposed methodology is applied to combine predictions from Effective Field Theories (EFTs) associated with a motivating nuclear physics application. Supplementary Material is available online. Source code is available at https://github.com/jcyannotty/OpenBT.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-28DOI: 10.1080/00401706.2023.2252476
Robert W. Mee, H. Li
{"title":"Inference for the Optimum using Linear Regression Models with Discrete Inputs","authors":"Robert W. Mee, H. Li","doi":"10.1080/00401706.2023.2252476","DOIUrl":"https://doi.org/10.1080/00401706.2023.2252476","url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44956453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.1080/00401706.2023.2242413
Guanqi Fang, Rong Pan
{"title":"A Class of Hierarchical Multivariate Wiener Processes for Modeling Dependent Degradation Data","authors":"Guanqi Fang, Rong Pan","doi":"10.1080/00401706.2023.2242413","DOIUrl":"https://doi.org/10.1080/00401706.2023.2242413","url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43926238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-26DOI: 10.1080/00401706.2023.2241523
X. Ye, Jiaxiang Cai, L. Tang, Z. Ye
{"title":"Statistical Modeling of the Effectiveness of Preventive Maintenance for Repairable Systems","authors":"X. Ye, Jiaxiang Cai, L. Tang, Z. Ye","doi":"10.1080/00401706.2023.2241523","DOIUrl":"https://doi.org/10.1080/00401706.2023.2241523","url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49473789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-03DOI: 10.1080/00401706.2023.2237825
Firdous Ahmad Mala
These tools and techniques rely heavily on additional concepts of algebraic geometry. In research on incidence theory, these tools and techniques could come extremely handy. However, anyone new to incidence theory may skip this chapter. All the chapters, except the second and the fourth, contain each a section, Open Problems, on open problems to incite further discussions and research in the subject. Besides, each chapter has a section, Exercises, to help readers check the extent of their understanding of the content. The book is a treasure of knowledge on incidence theory.
{"title":"Mathletics: How Gamblers, Managers, and Fans Use Mathematics in Sports, 2nd ed.","authors":"Firdous Ahmad Mala","doi":"10.1080/00401706.2023.2237825","DOIUrl":"https://doi.org/10.1080/00401706.2023.2237825","url":null,"abstract":"These tools and techniques rely heavily on additional concepts of algebraic geometry. In research on incidence theory, these tools and techniques could come extremely handy. However, anyone new to incidence theory may skip this chapter. All the chapters, except the second and the fourth, contain each a section, Open Problems, on open problems to incite further discussions and research in the subject. Besides, each chapter has a section, Exercises, to help readers check the extent of their understanding of the content. The book is a treasure of knowledge on incidence theory.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"65 1","pages":"450 - 451"},"PeriodicalIF":2.5,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43021492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-03DOI: 10.1080/00401706.2023.2237820
Suprianto, Nur Alam
{"title":"Mathematical Modeling in Biology: A Research Methods Approach, 1st ed.","authors":"Suprianto, Nur Alam","doi":"10.1080/00401706.2023.2237820","DOIUrl":"https://doi.org/10.1080/00401706.2023.2237820","url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"65 1","pages":"448 - 449"},"PeriodicalIF":2.5,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49640546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-03DOI: 10.1080/00401706.2023.2237826
Aminatus Sa'adah
and professionals. The style is so that mathematical jargon is not going to pose a threat to the readership of the book. Winston’s passion for both the subjects shines through in his writing, invigorating readers’ interest in the applications of mathematics beyond the classroom. The book encourages readers to view sports through a mathematical lens, prompting them to ask critical questions and challenge traditional assumptions. In conclusion, “Mathletics: How Gamblers, Managers, and Fans Use Mathematics in Sports” is a captivating and comprehensive exploration of the intersection between mathematics and sports. Wayne L. Winston’s adept storytelling, combined with his deep understanding of both fields, creates an engaging reading experience for anyone intrigued by the analytical side of sports. Whether you are a sports enthusiast, a budding mathematician, or simply curious about the inner workings of game strategy and player performance, this book is an excellent choice that will leave you with a newfound appreciation for the power of mathematics in the realm of sports.
{"title":"Artificial Intelligence with Python","authors":"Aminatus Sa'adah","doi":"10.1080/00401706.2023.2237826","DOIUrl":"https://doi.org/10.1080/00401706.2023.2237826","url":null,"abstract":"and professionals. The style is so that mathematical jargon is not going to pose a threat to the readership of the book. Winston’s passion for both the subjects shines through in his writing, invigorating readers’ interest in the applications of mathematics beyond the classroom. The book encourages readers to view sports through a mathematical lens, prompting them to ask critical questions and challenge traditional assumptions. In conclusion, “Mathletics: How Gamblers, Managers, and Fans Use Mathematics in Sports” is a captivating and comprehensive exploration of the intersection between mathematics and sports. Wayne L. Winston’s adept storytelling, combined with his deep understanding of both fields, creates an engaging reading experience for anyone intrigued by the analytical side of sports. Whether you are a sports enthusiast, a budding mathematician, or simply curious about the inner workings of game strategy and player performance, this book is an excellent choice that will leave you with a newfound appreciation for the power of mathematics in the realm of sports.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"65 1","pages":"451 - 452"},"PeriodicalIF":2.5,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45248494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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