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Processus de Markov

	Author(s):	Meyer P.-A.
	Collection:	Lecture Notes in Mathematics 0026
	Publisher:	Springer
	Year:	1967
	Language:	English
	Pages:	195 pages
	Size:	1.19 MB
	Extension:	DJVU

[tab] [content title="Description"]In mathematics , a Markov process is a stochastic process possessing the Markov property . In such a process, the prediction of the future from the present is not made more accurate by information about the past. Markov processes are named after their inventor, Andrei Markov . A discrete-time Markov process is a sequence{\displaystyle \scriptstyle \X_{1},}\scriptstyle\X_{1}, {\displaystyle \scriptstyle \X_{2},}\scriptstyle\X_{2}, {\displaystyle \scriptstyle \X_{3},\\dots }\scriptstyle\X_{3},\\dotsof random variables . The set of their possible values ​​is called the state space , the value{\displaystyle \scriptstyle \ X_{n}\ }\scriptstyle\X_{n}\being the state of the process at the moment{\displaystyle \scriptstyle \ n.}\scriptstyle\n.According to the authors, the term “ Markov chain ” designates discrete-time Markov processes or only discrete-time and discrete-state-space Markov processes, ie. discrete-time Markov processes whose state space is finite or countable . [/content] [content title="Content"] [/content] [content title="About the author"]Paul-André Meyer (1934-2003) is a French mathematician . Elected Correspondent of the Academy of Sciences onMarch 20, 1978, Mathematics section 1 . Mathematician, former student of the École Normale Supérieure , research director at the CNRS , Paul-André Meyer pursued a career as a researcher at the Louis Pasteur University in Strasbourg . He devoted his scientific work to the theory of potential and the calculus of probabilities , and distinguished himself by his renowned Strasbourg seminar. [/content] [/tab]

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