{"title":"关于预测合成公式的证明","authors":"Riku Masuda, Kaoru Irie","doi":"arxiv-2409.09660","DOIUrl":null,"url":null,"abstract":"Bayesian predictive synthesis is useful in synthesizing multiple predictive\ndistributions coherently. However, the proof for the fundamental equation of\nthe synthesized predictive density has been missing. In this technical report,\nwe review the series of research on predictive synthesis, then fill the gap\nbetween the known results and the equation used in modern applications. We\nprovide two proofs and clarify the structure of predictive synthesis.","PeriodicalId":501425,"journal":{"name":"arXiv - STAT - Methodology","volume":"17 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the Proofs of the Predictive Synthesis Formula\",\"authors\":\"Riku Masuda, Kaoru Irie\",\"doi\":\"arxiv-2409.09660\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Bayesian predictive synthesis is useful in synthesizing multiple predictive\\ndistributions coherently. However, the proof for the fundamental equation of\\nthe synthesized predictive density has been missing. In this technical report,\\nwe review the series of research on predictive synthesis, then fill the gap\\nbetween the known results and the equation used in modern applications. We\\nprovide two proofs and clarify the structure of predictive synthesis.\",\"PeriodicalId\":501425,\"journal\":{\"name\":\"arXiv - STAT - Methodology\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Methodology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.09660\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Methodology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.09660","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian predictive synthesis is useful in synthesizing multiple predictive
distributions coherently. However, the proof for the fundamental equation of
the synthesized predictive density has been missing. In this technical report,
we review the series of research on predictive synthesis, then fill the gap
between the known results and the equation used in modern applications. We
provide two proofs and clarify the structure of predictive synthesis.