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(MSC|60Jxx Category:Markov processes)
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<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m0625107.png" /></td> </tr></table>
 
<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m0625107.png" /></td> </tr></table>
  
with probability 1, that is, the conditional probability distribution of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m0625108.png" /> given <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m0625109.png" /> coincides (almost certainly) with the conditional distribution of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251010.png" /> given <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251011.png" />. This can be interpreted as independence of the "future" <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251012.png" /> and the "past" <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251013.png" /> given the fixed "present" <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251014.png" />. Stochastic processes satisfying the property (*) are called Markov processes (cf. [[Markov process|Markov process]]). The Markov property has (under certain additional assumptions) a stronger version, known as the "strong Markov property" . In discrete time <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251015.png" /> the strong Markov property, which is always true for (Markov) sequences satisfying (*), means that for each stopping time <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251016.png" /> (relative to the family of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251017.png" />-algebras <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251018.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251019.png" />), with probability one
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with probability 1, that is, the conditional probability distribution of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m0625108.png" /> given <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m0625109.png" /> coincides (almost certainly) with the conditional distribution of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251010.png" /> given <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251011.png" />. This can be interpreted as independence of the "future" <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251012.png" /> and the "past" <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251013.png" /> given the fixed "present" <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251014.png" />. Stochastic processes satisfying the property (*) are called Markov processes (cf. [[Markov process|Markov process]]). The Markov property has (under certain additional assumptions) a stronger version, known as the "strong Markov property" . In discrete time <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251015.png" /> the strong Markov property, which is always true for (Markov) sequences satisfying (*), means that for each stopping time <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251016.png" /> (relative to the family of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251017.png" />-algebras <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251018.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251019.png" />), with probability one
  
 
<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251020.png" /></td> </tr></table>
 
<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062510/m06251020.png" /></td> </tr></table>
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====References====
 
====References====
<table><TR><TD valign="top">[1]</TD> <TD valign="top"> I.I. [I.I. Gikhman] Gihman,   A.V. [A.V. Skorokhod] Skorohod,   "The theory of stochastic processes" , '''2''' , Springer (1975) (Translated from Russian)</TD></TR></table>
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<table><TR><TD valign="top">[1]</TD> <TD valign="top"> I.I. [I.I. Gikhman] Gihman, A.V. [A.V. Skorokhod] Skorohod, "The theory of stochastic processes" , '''2''' , Springer (1975) (Translated from Russian) {{MR|0375463}} {{ZBL|0305.60027}} </TD></TR></table>
  
  
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====References====
 
====References====
<table><TR><TD valign="top">[a1]</TD> <TD valign="top"> K.L. Chung,   "Markov chains with stationary transition probabilities" , Springer (1960)</TD></TR><TR><TD valign="top">[a2]</TD> <TD valign="top"> J.L. Doob,   "Stochastic processes" , Wiley (1953)</TD></TR><TR><TD valign="top">[a3]</TD> <TD valign="top"> E.B. Dynkin,   "Markov processes" , '''1''' , Springer (1965) (Translated from Russian)</TD></TR><TR><TD valign="top">[a4]</TD> <TD valign="top"> T.G. Kurtz,   "Markov processes" , Wiley (1986)</TD></TR><TR><TD valign="top">[a5]</TD> <TD valign="top"> W. Feller,   "An introduction to probability theory and its applications" , '''1–2''' , Wiley (1966)</TD></TR><TR><TD valign="top">[a6]</TD> <TD valign="top"> P. Lévy,   "Processus stochastiques et mouvement Brownien" , Gauthier-Villars (1965)</TD></TR><TR><TD valign="top">[a7]</TD> <TD valign="top"> M. Loève,   "Probability theory" , '''II''' , Springer (1978)</TD></TR></table>
+
<table><TR><TD valign="top">[a1]</TD> <TD valign="top"> K.L. Chung, "Markov chains with stationary transition probabilities" , Springer (1960) {{MR|0116388}} {{ZBL|0092.34304}} </TD></TR><TR><TD valign="top">[a2]</TD> <TD valign="top"> J.L. Doob, "Stochastic processes" , Wiley (1953) {{MR|1570654}} {{MR|0058896}} {{ZBL|0053.26802}} </TD></TR><TR><TD valign="top">[a3]</TD> <TD valign="top"> E.B. Dynkin, "Markov processes" , '''1''' , Springer (1965) (Translated from Russian) {{MR|0193671}} {{ZBL|0132.37901}} </TD></TR><TR><TD valign="top">[a4]</TD> <TD valign="top"> T.G. Kurtz, "Markov processes" , Wiley (1986) {{MR|0838085}} {{ZBL|0592.60049}} </TD></TR><TR><TD valign="top">[a5]</TD> <TD valign="top"> W. Feller, "An introduction to probability theory and its applications" , '''1–2''' , Wiley (1966) {{MR|0210154}} {{ZBL|0138.10207}} </TD></TR><TR><TD valign="top">[a6]</TD> <TD valign="top"> P. Lévy, "Processus stochastiques et mouvement Brownien" , Gauthier-Villars (1965) {{MR|0190953}} {{ZBL|0137.11602}} </TD></TR><TR><TD valign="top">[a7]</TD> <TD valign="top"> M. Loève, "Probability theory" , '''II''' , Springer (1978) {{MR|0651017}} {{MR|0651018}} {{ZBL|0385.60001}} </TD></TR></table>

Revision as of 10:31, 27 March 2012

for a real-valued stochastic process ,

2020 Mathematics Subject Classification: Primary: 60Jxx [MSN][ZBL]

The property that for any set of times from and any Borel set ,

(*)

with probability 1, that is, the conditional probability distribution of given coincides (almost certainly) with the conditional distribution of given . This can be interpreted as independence of the "future" and the "past" given the fixed "present" . Stochastic processes satisfying the property (*) are called Markov processes (cf. Markov process). The Markov property has (under certain additional assumptions) a stronger version, known as the "strong Markov property" . In discrete time the strong Markov property, which is always true for (Markov) sequences satisfying (*), means that for each stopping time (relative to the family of -algebras , ), with probability one

References

[1] I.I. [I.I. Gikhman] Gihman, A.V. [A.V. Skorokhod] Skorohod, "The theory of stochastic processes" , 2 , Springer (1975) (Translated from Russian) MR0375463 Zbl 0305.60027


Comments

References

[a1] K.L. Chung, "Markov chains with stationary transition probabilities" , Springer (1960) MR0116388 Zbl 0092.34304
[a2] J.L. Doob, "Stochastic processes" , Wiley (1953) MR1570654 MR0058896 Zbl 0053.26802
[a3] E.B. Dynkin, "Markov processes" , 1 , Springer (1965) (Translated from Russian) MR0193671 Zbl 0132.37901
[a4] T.G. Kurtz, "Markov processes" , Wiley (1986) MR0838085 Zbl 0592.60049
[a5] W. Feller, "An introduction to probability theory and its applications" , 1–2 , Wiley (1966) MR0210154 Zbl 0138.10207
[a6] P. Lévy, "Processus stochastiques et mouvement Brownien" , Gauthier-Villars (1965) MR0190953 Zbl 0137.11602
[a7] M. Loève, "Probability theory" , II , Springer (1978) MR0651017 MR0651018 Zbl 0385.60001
How to Cite This Entry:
Markov property. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Markov_property&oldid=21658
This article was adapted from an original article by A.N. Shiryaev (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article