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A set <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m0623701.png" /> of states of a homogeneous [[Markov chain|Markov chain]] <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m0623702.png" /> with state space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m0623703.png" /> such that
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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/m062370/m0623704.png" /></td> </tr></table>
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{{TEX|done}}
  
for any <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m0623705.png" />,
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{{MSC|60J10|60J27}}
  
<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/m062370/m0623706.png" /></td> </tr></table>
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[[Category:Markov processes]]
  
for any <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m0623707.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m0623708.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m0623709.png" />, and
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A set  $  K $
 +
of states of a homogeneous [[Markov chain|Markov chain]]  $  \xi ( t) $
 +
with state space  $  S $
 +
such that
  
<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/m062370/m06237010.png" /></td> <td valign="top" style="width:5%;text-align:right;">(*)</td></tr></table>
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$$
 +
{\mathsf P} \{ {\exists t > 0 } : {\xi ( t) = j \mid  \xi ( 0) = i } \}
 +
= 1
 +
$$
  
for any <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m06237011.png" />, where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m06237012.png" /> is the return time to the state <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m06237013.png" />:
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for any $  i , j \in K $,
  
<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/m062370/m06237014.png" /></td> </tr></table>
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$$
 +
p _ {il} ( t)  = \
 +
{\mathsf P} \{ \xi ( t) = l \mid  \xi ( 0) = i \}  =  0
 +
$$
 +
 
 +
for any  $  i \in K $,
 +
$  l \in S \setminus  K $,
 +
$  t > 0 $,
 +
and
 +
 
 +
$$ \tag{* }
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{\mathsf E} \tau _ {ii}  = \infty
 +
$$
 +
 
 +
for any  $  i \in K $,
 +
where  $  \tau _ {ii} $
 +
is the return time to the state  $  i $:
 +
 
 +
$$
 +
\tau _ {ii}  = \min \
 +
\{ {t > 0 } : {\xi ( t) = i \mid  \xi ( 0) = i } \}
 +
$$
  
 
for a discrete-time Markov chain, and
 
for a discrete-time Markov chain, and
  
<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/m062370/m06237015.png" /></td> </tr></table>
+
$$
 +
\tau _ {ii}  = \inf \
 +
\{ {t > 0 } : {\xi ( t) = i \mid  \xi ( 0) = i , \xi ( 0 + ) \neq i } \}
 +
$$
  
 
for a continuous-time Markov chain.
 
for a continuous-time Markov chain.
  
As in the case of a class of positive states (in the definition of a positive class (*) is replaced by <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m06237016.png" />), states belonging to the same zero class have a number of common properties. For example, for any states <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m06237017.png" /> of a zero class <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m06237018.png" />,
+
As in the case of a class of positive states (in the definition of a positive class (*) is replaced by $  {\mathsf E} \tau _ {ii} < \infty $),  
 +
states belonging to the same zero class have a number of common properties. For example, for any states $  i , j $
 +
of a zero class $  K $,
  
<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/m062370/m06237019.png" /></td> </tr></table>
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$$
 +
\lim\limits _ {t \rightarrow \infty }  p _ {ij} ( t)  = 0 .
 +
$$
  
 
An example of a Markov chain whose states form a single zero class is the symmetric random walk on the integers:
 
An example of a Markov chain whose states form a single zero class is the symmetric random walk on the integers:
  
<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/m062370/m06237020.png" /></td> </tr></table>
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$$
 +
\xi ( 0)  = 0 ,\ \
 +
\xi ( t)  = \xi ( t - 1 ) + \eta ( t) ,\ \
 +
t = 1 , 2 \dots
 +
$$
  
where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/m/m062/m062370/m06237021.png" /> are independent random variables,
+
where $  \eta ( 1) , \eta ( 2) \dots $
 +
are independent random variables,
  
<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/m062370/m06237022.png" /></td> </tr></table>
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$$
 +
{\mathsf P} \{ \eta ( i) = 1 \} \
 +
= {\mathsf P} \{ \eta ( i) = - 1 \} \
 +
= 1/2 ,\  i = 1 , 2 ,\dots .
 +
$$
  
 
====References====
 
====References====
<table><TR><TD valign="top">[1]</TD> <TD valign="top"K.L. Chung,   "Markov chains with stationary transition probabilities" , Springer (1967)</TD></TR></table>
+
{|
 
+
|valign="top"|{{Ref|C}}|| K.L. Chung, "Markov chains with stationary transition probabilities" , Springer (1967) {{MR|0217872}} {{ZBL|0146.38401}}
 
+
|}
  
 
====Comments====
 
====Comments====
Line 42: Line 93:
  
 
====References====
 
====References====
<table><TR><TD valign="top">[a1]</TD> <TD valign="top">  W. Feller,   "An introduction to probability theory and its applications" , '''1–2''' , Wiley (1966)</TD></TR><TR><TD valign="top">[a2]</TD> <TD valign="top">  D. Freedman,   "Markov chains" , Holden-Day (1975)</TD></TR><TR><TD valign="top">[a3]</TD> <TD valign="top">  M. Iosifescu,   "Finite Markov processes and their applications" , Wiley (1980)</TD></TR><TR><TD valign="top">[a4]</TD> <TD valign="top"J.G. Kemeny,   J.L. Snell,   "Finite Markov chains" , v. Nostrand (1960)</TD></TR><TR><TD valign="top">[a5]</TD> <TD valign="top"J.G. Kemeny,   J.L. Snell,   A.W. Knapp,   "Denumerable Markov chains" , Springer (1976)</TD></TR><TR><TD valign="top">[a6]</TD> <TD valign="top">  D. Revuz,   "Markov chains" , North-Holland (1975)</TD></TR><TR><TD valign="top">[a7]</TD> <TD valign="top"V.I. [V.I. Romanovskii] Romanovsky,   "Discrete Markov chains" , Wolters-Noordhoff (1970) (Translated from Russian)</TD></TR><TR><TD valign="top">[a8]</TD> <TD valign="top"E. Seneta,   "Non-negative matrices and Markov chains" , Springer (1981)</TD></TR></table>
+
{|
 +
|valign="top"|{{Ref|F}}|| W. Feller, [[Feller, "An introduction to probability theory and its  applications"|"An introduction to probability theory and its applications"]], '''1–2''', Wiley (1966)
 +
|-
 +
|valign="top"|{{Ref|Fr}}|| D. Freedman, "Markov chains", Holden-Day (1975) {{MR|0686269}} {{MR|0681291}} {{MR|0556418}} {{MR|0428472}} {{MR|0292176}} {{MR|0237001}} {{MR|0211464}} {{MR|0164375}} {{MR|0158435}} {{MR|0152015}} {{ZBL|0501.60071}} {{ZBL|0501.60069}} {{ZBL|0426.60064}} {{ZBL|0325.60059}} {{ZBL|0322.60057}} {{ZBL|0212.49801}} {{ZBL|0129.30605}}
 +
|-
 +
|valign="top"|{{Ref|I}}|| M. Iosifescu, "Finite Markov processes and their applications", Wiley (1980) {{MR|0587116}} {{ZBL|0436.60001}}
 +
|-
 +
|valign="top"|{{Ref|KS}}|| J.G. Kemeny, J.L. Snell, "Finite Markov chains", v. Nostrand (1960) {{MR|1531032}} {{MR|0115196}} {{ZBL|0089.13704}}
 +
|-
 +
|valign="top"|{{Ref|KSK}}|| J.G. Kemeny, J.L. Snell, A.W. Knapp, "Denumerable Markov chains", Springer (1976) {{MR|0407981}} {{ZBL|0348.60090}}
 +
|-
 +
|valign="top"|{{Ref|Re}}|| D. Revuz, "Markov chains", North-Holland (1975) {{MR|0415773}} {{ZBL|0332.60045}}
 +
|-
 +
|valign="top"|{{Ref|Ro}}|| V.I. Romanovsky, "Discrete Markov chains", Wolters-Noordhoff (1970) (Translated from Russian) {{MR|0266312}} {{ZBL|0201.20002}}
 +
|-
 +
|valign="top"|{{Ref|S}}|| E. Seneta, "Non-negative matrices and Markov chains", Springer (1981) {{MR|2209438}} {{ZBL|0471.60001}}
 +
|}

Latest revision as of 07:59, 6 June 2020


2020 Mathematics Subject Classification: Primary: 60J10 Secondary: 60J27 [MSN][ZBL]

A set $ K $ of states of a homogeneous Markov chain $ \xi ( t) $ with state space $ S $ such that

$$ {\mathsf P} \{ {\exists t > 0 } : {\xi ( t) = j \mid \xi ( 0) = i } \} = 1 $$

for any $ i , j \in K $,

$$ p _ {il} ( t) = \ {\mathsf P} \{ \xi ( t) = l \mid \xi ( 0) = i \} = 0 $$

for any $ i \in K $, $ l \in S \setminus K $, $ t > 0 $, and

$$ \tag{* } {\mathsf E} \tau _ {ii} = \infty $$

for any $ i \in K $, where $ \tau _ {ii} $ is the return time to the state $ i $:

$$ \tau _ {ii} = \min \ \{ {t > 0 } : {\xi ( t) = i \mid \xi ( 0) = i } \} $$

for a discrete-time Markov chain, and

$$ \tau _ {ii} = \inf \ \{ {t > 0 } : {\xi ( t) = i \mid \xi ( 0) = i , \xi ( 0 + ) \neq i } \} $$

for a continuous-time Markov chain.

As in the case of a class of positive states (in the definition of a positive class (*) is replaced by $ {\mathsf E} \tau _ {ii} < \infty $), states belonging to the same zero class have a number of common properties. For example, for any states $ i , j $ of a zero class $ K $,

$$ \lim\limits _ {t \rightarrow \infty } p _ {ij} ( t) = 0 . $$

An example of a Markov chain whose states form a single zero class is the symmetric random walk on the integers:

$$ \xi ( 0) = 0 ,\ \ \xi ( t) = \xi ( t - 1 ) + \eta ( t) ,\ \ t = 1 , 2 \dots $$

where $ \eta ( 1) , \eta ( 2) \dots $ are independent random variables,

$$ {\mathsf P} \{ \eta ( i) = 1 \} \ = {\mathsf P} \{ \eta ( i) = - 1 \} \ = 1/2 ,\ i = 1 , 2 ,\dots . $$

References

[C] K.L. Chung, "Markov chains with stationary transition probabilities" , Springer (1967) MR0217872 Zbl 0146.38401

Comments

Cf. also Markov chain, class of positive states of a.

References

[F] W. Feller, "An introduction to probability theory and its applications", 1–2, Wiley (1966)
[Fr] D. Freedman, "Markov chains", Holden-Day (1975) MR0686269 MR0681291 MR0556418 MR0428472 MR0292176 MR0237001 MR0211464 MR0164375 MR0158435 MR0152015 Zbl 0501.60071 Zbl 0501.60069 Zbl 0426.60064 Zbl 0325.60059 Zbl 0322.60057 Zbl 0212.49801 Zbl 0129.30605
[I] M. Iosifescu, "Finite Markov processes and their applications", Wiley (1980) MR0587116 Zbl 0436.60001
[KS] J.G. Kemeny, J.L. Snell, "Finite Markov chains", v. Nostrand (1960) MR1531032 MR0115196 Zbl 0089.13704
[KSK] J.G. Kemeny, J.L. Snell, A.W. Knapp, "Denumerable Markov chains", Springer (1976) MR0407981 Zbl 0348.60090
[Re] D. Revuz, "Markov chains", North-Holland (1975) MR0415773 Zbl 0332.60045
[Ro] V.I. Romanovsky, "Discrete Markov chains", Wolters-Noordhoff (1970) (Translated from Russian) MR0266312 Zbl 0201.20002
[S] E. Seneta, "Non-negative matrices and Markov chains", Springer (1981) MR2209438 Zbl 0471.60001
How to Cite This Entry:
Markov chain, class of zero states of a. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Markov_chain,_class_of_zero_states_of_a&oldid=18811
This article was adapted from an original article by A.M. Zubkov (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article