{"title":"三种统计技术在生物成像和建模方面具有很高的潜力。","authors":"M Fridman, J M Steele","doi":"10.1007/978-1-4757-9788-6_15","DOIUrl":null,"url":null,"abstract":"<p><p>The three technologies that are surveyed here are (1) wavelet approximations, (2) hidden Markov models, and (3) the Markov chain Renaissance. The intention of the article is to provide an introduction to the benefits these technologies offer and to explain as far as possible the sources of their effectiveness. We also hope to suggest some useful relationships between these technologies and issues of importance on the agenda of biological and medical research.</p>","PeriodicalId":8697,"journal":{"name":"Basic life sciences","volume":"63 ","pages":"199-224"},"PeriodicalIF":0.0000,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Three statistical technologies with high potential in biological imaging and modeling.\",\"authors\":\"M Fridman, J M Steele\",\"doi\":\"10.1007/978-1-4757-9788-6_15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The three technologies that are surveyed here are (1) wavelet approximations, (2) hidden Markov models, and (3) the Markov chain Renaissance. The intention of the article is to provide an introduction to the benefits these technologies offer and to explain as far as possible the sources of their effectiveness. We also hope to suggest some useful relationships between these technologies and issues of importance on the agenda of biological and medical research.</p>\",\"PeriodicalId\":8697,\"journal\":{\"name\":\"Basic life sciences\",\"volume\":\"63 \",\"pages\":\"199-224\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Basic life sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/978-1-4757-9788-6_15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Basic life sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-1-4757-9788-6_15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Three statistical technologies with high potential in biological imaging and modeling.
The three technologies that are surveyed here are (1) wavelet approximations, (2) hidden Markov models, and (3) the Markov chain Renaissance. The intention of the article is to provide an introduction to the benefits these technologies offer and to explain as far as possible the sources of their effectiveness. We also hope to suggest some useful relationships between these technologies and issues of importance on the agenda of biological and medical research.
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