Difference between revisions of "Markov moment"
Ulf Rehmann (talk | contribs) m (MR/ZBL numbers added) |
(refs format) |
||
Line 39: | Line 39: | ||
====References==== | ====References==== | ||
− | + | {| | |
− | + | |valign="top"|{{Ref|GS}}|| I.I. Gihman, A.V. [A.V. Skorokhod] Skorohod, "The theory of stochastic processes" , '''2''' , Springer (1975) (Translated from Russian) {{MR|0375463}} {{ZBL|0305.60027}} | |
− | + | |} | |
====Comments==== | ====Comments==== | ||
Line 47: | Line 47: | ||
====References==== | ====References==== | ||
− | + | {| | |
+ | |valign="top"|{{Ref|BG}}|| R.M. Blumenthal, R.K. Getoor, "Markov processes and potential theory" , Acad. Press (1968) {{MR|0264757}} {{ZBL|0169.49204}} | ||
+ | |- | ||
+ | |valign="top"|{{Ref|Do}}|| J.L. Doob, "Classical potential theory and its probabilistic counterpart" , Springer (1984) pp. 390 {{MR|0731258}} {{ZBL|0549.31001}} | ||
+ | |- | ||
+ | |valign="top"|{{Ref|Dy}}|| E.B. Dynkin, "Markov processes" , '''1''' , Springer (1965) (Translated from Russian) {{MR|0193671}} {{ZBL|0132.37901}} | ||
+ | |- | ||
+ | |valign="top"|{{Ref|W}}|| A.D. Wentzell, "A course in the theory of stochastic processes" , McGraw-Hill (1981) (Translated from Russian) {{MR|0781738}} {{MR|0614594}} {{ZBL|0502.60001}} | ||
+ | |- | ||
+ | |valign="top"|{{Ref|B}}|| L.P. Breiman, "Probability" , Addison-Wesley (1968) {{MR|0229267}} {{ZBL|0174.48801}} | ||
+ | |} |
Revision as of 06:28, 14 May 2012
Markov time; stopping time
2020 Mathematics Subject Classification: Primary: 60G40 [MSN][ZBL]
A notion used in probability theory for random variables having the property of independence of the "future" . More precisely, let be a measurable space with a non-decreasing family , , of -algebras of ( in the case of continuous time and in the case of discrete time). A random variable with values in is called a Markov moment or Markov time (relative to the family , ) if for each the event belongs to . In the case of discrete time this is equivalent to saying that for any the event belongs to .
Examples.
1) Let , , be a real-valued right-continuous random process given on , and let . Then the random variables
and
that is, the (first and first after ) times of hitting the (Borel) set , form Markov moments (in the case it is assumed that ).
2) If , , is a standard Wiener process, then the Markov moment
has probability density
Here , but .
3) The random variable
being the first time after which remains in , is an example of a non-Markov moment (a random variable depending on the "future" ).
Using the idea of a Markov moment one can formulate the strong Markov property of Markov processes (cf. Markov process). Markov moments and stopping times (that is, finite Markov moments) play a major role in the general theory of random processes and statistical sequential analysis.
References
[GS] | I.I. Gihman, A.V. [A.V. Skorokhod] Skorohod, "The theory of stochastic processes" , 2 , Springer (1975) (Translated from Russian) MR0375463 Zbl 0305.60027 |
Comments
References
[BG] | R.M. Blumenthal, R.K. Getoor, "Markov processes and potential theory" , Acad. Press (1968) MR0264757 Zbl 0169.49204 |
[Do] | J.L. Doob, "Classical potential theory and its probabilistic counterpart" , Springer (1984) pp. 390 MR0731258 Zbl 0549.31001 |
[Dy] | E.B. Dynkin, "Markov processes" , 1 , Springer (1965) (Translated from Russian) MR0193671 Zbl 0132.37901 |
[W] | A.D. Wentzell, "A course in the theory of stochastic processes" , McGraw-Hill (1981) (Translated from Russian) MR0781738 MR0614594 Zbl 0502.60001 |
[B] | L.P. Breiman, "Probability" , Addison-Wesley (1968) MR0229267 Zbl 0174.48801 |
Markov moment. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Markov_moment&oldid=26568