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Theory of Probability and Its Applications
Article . 1956 . Peer-reviewed
Data sources: Crossref
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Infinitesimal Operators of Markov Processes

Infinitesimal operators of Markov processes
Authors: Dynkin, E. B.;

Infinitesimal Operators of Markov Processes

Abstract

In 1931 A. Kolmogorov showed [9] that a wide class of one dimensional Markov processes can be described by the differential equation \[(1)\qquad \frac{{\partial u}}{{\partial t}} + a\frac{{\partial ^2 u}}{{\partial x^2 }} + b\frac{{\partial u}}{{\partial x}}.\]Are there one-dimensional Markov processes governed by equations of the type \[ (2)\qquad \frac{{\partial u}}{{\partial t}}\mathfrak{A}u, \] where $\mathfrak{A}$ is a differential operator of an order higher than 2? This question remained unsettled until 1954–1955 when it was completely solved by W. Feller. Feller showed that every one-dimensional Markov process with a continuous path function is described by (2), where $\mathfrak{A}$ is the generalized second derivative.The purely analytical method of Feller is essentially connected with the one-dimensional character of the problem. It is very difficult to extend this method to the case of two (and more) dimensions.In this paper, a method is developed which can be applied to n dimensions as well as...

Keywords

Probability Theory

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Average
Top 1%
Top 10%
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