{"title":"马尔可夫随机场","authors":"E. Wong","doi":"10.1109/CDC.1984.272296","DOIUrl":null,"url":null,"abstract":"In this paper we examine the Markovian properties of three important random fields: Lévy's Brownian motion, free Euclidean field, and Wiener process. In so doing, we advance the proposition that appropriate candidates for Markov fields are stochastic differential forms and their Markovian property is characterized by being \"one derivative\" removed from white noise.","PeriodicalId":269680,"journal":{"name":"The 23rd IEEE Conference on Decision and Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1984-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Markovian random fields\",\"authors\":\"E. Wong\",\"doi\":\"10.1109/CDC.1984.272296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we examine the Markovian properties of three important random fields: Lévy's Brownian motion, free Euclidean field, and Wiener process. In so doing, we advance the proposition that appropriate candidates for Markov fields are stochastic differential forms and their Markovian property is characterized by being \\\"one derivative\\\" removed from white noise.\",\"PeriodicalId\":269680,\"journal\":{\"name\":\"The 23rd IEEE Conference on Decision and Control\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1984-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 23rd IEEE Conference on Decision and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1984.272296\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 23rd IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1984.272296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we examine the Markovian properties of three important random fields: Lévy's Brownian motion, free Euclidean field, and Wiener process. In so doing, we advance the proposition that appropriate candidates for Markov fields are stochastic differential forms and their Markovian property is characterized by being "one derivative" removed from white noise.
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