{"title":"福克尔普朗克方程","authors":"S. Loos","doi":"10.1142/9789811213007_0002","DOIUrl":null,"url":null,"abstract":"In the preceding chapter, we have introduced the Langevin equation, which describes the random processes studied in this thesis on a stochastic level. For Markovian systems, it is well known that Fokker-Planck equations (FPE) provide a complementary way of description, on the probabilistic level. These are deterministic equations, whose solutions are probability density functions. In the following, we will briefly introduce this concept, first focusing on the Markovian case.","PeriodicalId":291068,"journal":{"name":"Stochastic Systems with Time Delay","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fokker-Planck Equations\",\"authors\":\"S. Loos\",\"doi\":\"10.1142/9789811213007_0002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the preceding chapter, we have introduced the Langevin equation, which describes the random processes studied in this thesis on a stochastic level. For Markovian systems, it is well known that Fokker-Planck equations (FPE) provide a complementary way of description, on the probabilistic level. These are deterministic equations, whose solutions are probability density functions. In the following, we will briefly introduce this concept, first focusing on the Markovian case.\",\"PeriodicalId\":291068,\"journal\":{\"name\":\"Stochastic Systems with Time Delay\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stochastic Systems with Time Delay\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/9789811213007_0002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastic Systems with Time Delay","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/9789811213007_0002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In the preceding chapter, we have introduced the Langevin equation, which describes the random processes studied in this thesis on a stochastic level. For Markovian systems, it is well known that Fokker-Planck equations (FPE) provide a complementary way of description, on the probabilistic level. These are deterministic equations, whose solutions are probability density functions. In the following, we will briefly introduce this concept, first focusing on the Markovian case.