How do I show that the mean recurrence time for transient states is infinity?

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The random variable $T_i$, the "Hitting Time of $i$" is defined to be the first $n$ such that $X_n=i$ given that $X_0=i$.



By the mean recurrence time of $T_i$, I mean the expected value of this random variable.



I wish to show that if $i$ is transient, then the expectation does not converge to any finite real number. While this, intuitively makes sense, I do not know how to formally prove this and any help is appreciated.






probability markov-chains







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  • $begingroup$
    A given state $i$ is transient iff $P_i(T_i=+infty)ne0$. Then $E_i(T_i)geqslant E_i(T_imathbf 1_{T_i=+infty})=+inftycdot P_i(T_i=+infty)=+infty$, as desired.
    $endgroup$
    – Did
    Jan 9 at 0:07
















0












$begingroup$


The random variable $T_i$, the "Hitting Time of $i$" is defined to be the first $n$ such that $X_n=i$ given that $X_0=i$.



By the mean recurrence time of $T_i$, I mean the expected value of this random variable.



I wish to show that if $i$ is transient, then the expectation does not converge to any finite real number. While this, intuitively makes sense, I do not know how to formally prove this and any help is appreciated.






probability markov-chains







share|cite|improve this question









$endgroup$












  • $begingroup$
    A given state $i$ is transient iff $P_i(T_i=+infty)ne0$. Then $E_i(T_i)geqslant E_i(T_imathbf 1_{T_i=+infty})=+inftycdot P_i(T_i=+infty)=+infty$, as desired.
    $endgroup$
    – Did
    Jan 9 at 0:07














0












0








0





$begingroup$


The random variable $T_i$, the "Hitting Time of $i$" is defined to be the first $n$ such that $X_n=i$ given that $X_0=i$.



By the mean recurrence time of $T_i$, I mean the expected value of this random variable.



I wish to show that if $i$ is transient, then the expectation does not converge to any finite real number. While this, intuitively makes sense, I do not know how to formally prove this and any help is appreciated.






probability markov-chains







share|cite|improve this question









$endgroup$




The random variable $T_i$, the "Hitting Time of $i$" is defined to be the first $n$ such that $X_n=i$ given that $X_0=i$.



By the mean recurrence time of $T_i$, I mean the expected value of this random variable.



I wish to show that if $i$ is transient, then the expectation does not converge to any finite real number. While this, intuitively makes sense, I do not know how to formally prove this and any help is appreciated.






probability markov-chains




probability markov-chains






share|cite|improve this question













share|cite|improve this question











share|cite|improve this question




share|cite|improve this question










asked Nov 14 '18 at 18:24









Agnishom ChattopadhyayAgnishom Chattopadhyay

1,6121818




1,6121818












  • $begingroup$
    A given state $i$ is transient iff $P_i(T_i=+infty)ne0$. Then $E_i(T_i)geqslant E_i(T_imathbf 1_{T_i=+infty})=+inftycdot P_i(T_i=+infty)=+infty$, as desired.
    $endgroup$
    – Did
    Jan 9 at 0:07


















  • $begingroup$
    A given state $i$ is transient iff $P_i(T_i=+infty)ne0$. Then $E_i(T_i)geqslant E_i(T_imathbf 1_{T_i=+infty})=+inftycdot P_i(T_i=+infty)=+infty$, as desired.
    $endgroup$
    – Did
    Jan 9 at 0:07
















$begingroup$
A given state $i$ is transient iff $P_i(T_i=+infty)ne0$. Then $E_i(T_i)geqslant E_i(T_imathbf 1_{T_i=+infty})=+inftycdot P_i(T_i=+infty)=+infty$, as desired.
$endgroup$
– Did
Jan 9 at 0:07




$begingroup$
A given state $i$ is transient iff $P_i(T_i=+infty)ne0$. Then $E_i(T_i)geqslant E_i(T_imathbf 1_{T_i=+infty})=+inftycdot P_i(T_i=+infty)=+infty$, as desired.
$endgroup$
– Did
Jan 9 at 0:07










2 Answers
2






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oldest

votes


















1












$begingroup$

Note that state $i$ is persistent iff,
$$ P(X_n = i text{ for some } n geq 1| X_0 = i) = 1$$



Each state is transient or persistent.

The hitting time of state $i$, $T_i$, is a random variable defined as the first time we visit state $i$:
$$ T_i = min {n | n geq 1, X_n = i} $$
where $T_i$ is defined as $infty$ if this visit never happens.



We now show that $ P(T_i = infty | X_0 = i) > 0 $ iff state $i$ is transient.
Then, the required result on the mean recurrence time follows, because the mean recurrence time $mu_i$ is defined as:
$$mu_i = E(T_i| X_0 = i) $$



Suppose state $i$ is transient. Then,
$$
begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
& = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
& > 1 - 1 = 0.
end{align}
$$



Suppose state $i$ is persistent. Then,
$$
begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
& = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
& = 1 - 1 = 0.
end{align}
$$



This shows both directions, and completes the proof.






share|cite|improve this answer









$endgroup$





















    0












    $begingroup$

    I don't think you have the definition quite right. The first passage time $T_i$ is the minimum $n$ such that $X_n = i$ given $X_0 = i$, and is defined to be $infty$ if no such $n$ exists.



    In that light, we just need to know that a state $i$ is transient if there's some other state $j$ such that $i rightarrow j$, but $j notrightarrow i$. That is, if a (finite-state, discrete-time) Markov chain initialized at state $i$ reaches state $j$ it almost surely never returns to $i$. Because $i rightarrow j$, this happens with positive probability and thus $T_i = infty$ with positive probability. (Which implies $mathbb{E}[T_i] = infty$).



    One reference you may find useful is these lecture notes.






    share|cite|improve this answer











    $endgroup$













    • $begingroup$
      Your definition of transience is wrong. Most transient Markov chains are such that $ito j$ and $jto i$ for every $i$ and $j$ in their state space.
      $endgroup$
      – Did
      Jan 9 at 0:06










    • $begingroup$
      But only in the case of an infinite state space, right? I was only talking about the finite-state case (and probably should have said that more clearly).
      $endgroup$
      – Will Brannon
      Jan 9 at 3:52










    • $begingroup$
      Then you are describing (non-)communicability between states rather than transience.
      $endgroup$
      – Did
      Jan 9 at 6:48











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    2 Answers
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    2 Answers
    2






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    active

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    active

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    1












    $begingroup$

    Note that state $i$ is persistent iff,
    $$ P(X_n = i text{ for some } n geq 1| X_0 = i) = 1$$



    Each state is transient or persistent.

    The hitting time of state $i$, $T_i$, is a random variable defined as the first time we visit state $i$:
    $$ T_i = min {n | n geq 1, X_n = i} $$
    where $T_i$ is defined as $infty$ if this visit never happens.



    We now show that $ P(T_i = infty | X_0 = i) > 0 $ iff state $i$ is transient.
    Then, the required result on the mean recurrence time follows, because the mean recurrence time $mu_i$ is defined as:
    $$mu_i = E(T_i| X_0 = i) $$



    Suppose state $i$ is transient. Then,
    $$
    begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
    & = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
    & > 1 - 1 = 0.
    end{align}
    $$



    Suppose state $i$ is persistent. Then,
    $$
    begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
    & = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
    & = 1 - 1 = 0.
    end{align}
    $$



    This shows both directions, and completes the proof.






    share|cite|improve this answer









    $endgroup$


















      1












      $begingroup$

      Note that state $i$ is persistent iff,
      $$ P(X_n = i text{ for some } n geq 1| X_0 = i) = 1$$



      Each state is transient or persistent.

      The hitting time of state $i$, $T_i$, is a random variable defined as the first time we visit state $i$:
      $$ T_i = min {n | n geq 1, X_n = i} $$
      where $T_i$ is defined as $infty$ if this visit never happens.



      We now show that $ P(T_i = infty | X_0 = i) > 0 $ iff state $i$ is transient.
      Then, the required result on the mean recurrence time follows, because the mean recurrence time $mu_i$ is defined as:
      $$mu_i = E(T_i| X_0 = i) $$



      Suppose state $i$ is transient. Then,
      $$
      begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
      & = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
      & > 1 - 1 = 0.
      end{align}
      $$



      Suppose state $i$ is persistent. Then,
      $$
      begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
      & = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
      & = 1 - 1 = 0.
      end{align}
      $$



      This shows both directions, and completes the proof.






      share|cite|improve this answer









      $endgroup$
















        1












        1








        1





        $begingroup$

        Note that state $i$ is persistent iff,
        $$ P(X_n = i text{ for some } n geq 1| X_0 = i) = 1$$



        Each state is transient or persistent.

        The hitting time of state $i$, $T_i$, is a random variable defined as the first time we visit state $i$:
        $$ T_i = min {n | n geq 1, X_n = i} $$
        where $T_i$ is defined as $infty$ if this visit never happens.



        We now show that $ P(T_i = infty | X_0 = i) > 0 $ iff state $i$ is transient.
        Then, the required result on the mean recurrence time follows, because the mean recurrence time $mu_i$ is defined as:
        $$mu_i = E(T_i| X_0 = i) $$



        Suppose state $i$ is transient. Then,
        $$
        begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
        & = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
        & > 1 - 1 = 0.
        end{align}
        $$



        Suppose state $i$ is persistent. Then,
        $$
        begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
        & = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
        & = 1 - 1 = 0.
        end{align}
        $$



        This shows both directions, and completes the proof.






        share|cite|improve this answer









        $endgroup$



        Note that state $i$ is persistent iff,
        $$ P(X_n = i text{ for some } n geq 1| X_0 = i) = 1$$



        Each state is transient or persistent.

        The hitting time of state $i$, $T_i$, is a random variable defined as the first time we visit state $i$:
        $$ T_i = min {n | n geq 1, X_n = i} $$
        where $T_i$ is defined as $infty$ if this visit never happens.



        We now show that $ P(T_i = infty | X_0 = i) > 0 $ iff state $i$ is transient.
        Then, the required result on the mean recurrence time follows, because the mean recurrence time $mu_i$ is defined as:
        $$mu_i = E(T_i| X_0 = i) $$



        Suppose state $i$ is transient. Then,
        $$
        begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
        & = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
        & > 1 - 1 = 0.
        end{align}
        $$



        Suppose state $i$ is persistent. Then,
        $$
        begin{align} P(T_i = infty | X_0 = i) & = P(X_n neq i text{ for all } n geq 1 | X_0 = i) \
        & = 1 - P(X_n = i text{ for some } n geq 1 | X_0 = i) \
        & = 1 - 1 = 0.
        end{align}
        $$



        This shows both directions, and completes the proof.







        share|cite|improve this answer












        share|cite|improve this answer



        share|cite|improve this answer










        answered Feb 13 at 7:00









        AmeyaAmeya

        113




        113























            0












            $begingroup$

            I don't think you have the definition quite right. The first passage time $T_i$ is the minimum $n$ such that $X_n = i$ given $X_0 = i$, and is defined to be $infty$ if no such $n$ exists.



            In that light, we just need to know that a state $i$ is transient if there's some other state $j$ such that $i rightarrow j$, but $j notrightarrow i$. That is, if a (finite-state, discrete-time) Markov chain initialized at state $i$ reaches state $j$ it almost surely never returns to $i$. Because $i rightarrow j$, this happens with positive probability and thus $T_i = infty$ with positive probability. (Which implies $mathbb{E}[T_i] = infty$).



            One reference you may find useful is these lecture notes.






            share|cite|improve this answer











            $endgroup$













            • $begingroup$
              Your definition of transience is wrong. Most transient Markov chains are such that $ito j$ and $jto i$ for every $i$ and $j$ in their state space.
              $endgroup$
              – Did
              Jan 9 at 0:06










            • $begingroup$
              But only in the case of an infinite state space, right? I was only talking about the finite-state case (and probably should have said that more clearly).
              $endgroup$
              – Will Brannon
              Jan 9 at 3:52










            • $begingroup$
              Then you are describing (non-)communicability between states rather than transience.
              $endgroup$
              – Did
              Jan 9 at 6:48
















            0












            $begingroup$

            I don't think you have the definition quite right. The first passage time $T_i$ is the minimum $n$ such that $X_n = i$ given $X_0 = i$, and is defined to be $infty$ if no such $n$ exists.



            In that light, we just need to know that a state $i$ is transient if there's some other state $j$ such that $i rightarrow j$, but $j notrightarrow i$. That is, if a (finite-state, discrete-time) Markov chain initialized at state $i$ reaches state $j$ it almost surely never returns to $i$. Because $i rightarrow j$, this happens with positive probability and thus $T_i = infty$ with positive probability. (Which implies $mathbb{E}[T_i] = infty$).



            One reference you may find useful is these lecture notes.






            share|cite|improve this answer











            $endgroup$













            • $begingroup$
              Your definition of transience is wrong. Most transient Markov chains are such that $ito j$ and $jto i$ for every $i$ and $j$ in their state space.
              $endgroup$
              – Did
              Jan 9 at 0:06










            • $begingroup$
              But only in the case of an infinite state space, right? I was only talking about the finite-state case (and probably should have said that more clearly).
              $endgroup$
              – Will Brannon
              Jan 9 at 3:52










            • $begingroup$
              Then you are describing (non-)communicability between states rather than transience.
              $endgroup$
              – Did
              Jan 9 at 6:48














            0












            0








            0





            $begingroup$

            I don't think you have the definition quite right. The first passage time $T_i$ is the minimum $n$ such that $X_n = i$ given $X_0 = i$, and is defined to be $infty$ if no such $n$ exists.



            In that light, we just need to know that a state $i$ is transient if there's some other state $j$ such that $i rightarrow j$, but $j notrightarrow i$. That is, if a (finite-state, discrete-time) Markov chain initialized at state $i$ reaches state $j$ it almost surely never returns to $i$. Because $i rightarrow j$, this happens with positive probability and thus $T_i = infty$ with positive probability. (Which implies $mathbb{E}[T_i] = infty$).



            One reference you may find useful is these lecture notes.






            share|cite|improve this answer











            $endgroup$



            I don't think you have the definition quite right. The first passage time $T_i$ is the minimum $n$ such that $X_n = i$ given $X_0 = i$, and is defined to be $infty$ if no such $n$ exists.



            In that light, we just need to know that a state $i$ is transient if there's some other state $j$ such that $i rightarrow j$, but $j notrightarrow i$. That is, if a (finite-state, discrete-time) Markov chain initialized at state $i$ reaches state $j$ it almost surely never returns to $i$. Because $i rightarrow j$, this happens with positive probability and thus $T_i = infty$ with positive probability. (Which implies $mathbb{E}[T_i] = infty$).



            One reference you may find useful is these lecture notes.







            share|cite|improve this answer














            share|cite|improve this answer



            share|cite|improve this answer








            edited Jan 8 at 23:42

























            answered Jan 8 at 23:36









            Will BrannonWill Brannon

            111




            111












            • $begingroup$
              Your definition of transience is wrong. Most transient Markov chains are such that $ito j$ and $jto i$ for every $i$ and $j$ in their state space.
              $endgroup$
              – Did
              Jan 9 at 0:06










            • $begingroup$
              But only in the case of an infinite state space, right? I was only talking about the finite-state case (and probably should have said that more clearly).
              $endgroup$
              – Will Brannon
              Jan 9 at 3:52










            • $begingroup$
              Then you are describing (non-)communicability between states rather than transience.
              $endgroup$
              – Did
              Jan 9 at 6:48


















            • $begingroup$
              Your definition of transience is wrong. Most transient Markov chains are such that $ito j$ and $jto i$ for every $i$ and $j$ in their state space.
              $endgroup$
              – Did
              Jan 9 at 0:06










            • $begingroup$
              But only in the case of an infinite state space, right? I was only talking about the finite-state case (and probably should have said that more clearly).
              $endgroup$
              – Will Brannon
              Jan 9 at 3:52










            • $begingroup$
              Then you are describing (non-)communicability between states rather than transience.
              $endgroup$
              – Did
              Jan 9 at 6:48
















            $begingroup$
            Your definition of transience is wrong. Most transient Markov chains are such that $ito j$ and $jto i$ for every $i$ and $j$ in their state space.
            $endgroup$
            – Did
            Jan 9 at 0:06




            $begingroup$
            Your definition of transience is wrong. Most transient Markov chains are such that $ito j$ and $jto i$ for every $i$ and $j$ in their state space.
            $endgroup$
            – Did
            Jan 9 at 0:06












            $begingroup$
            But only in the case of an infinite state space, right? I was only talking about the finite-state case (and probably should have said that more clearly).
            $endgroup$
            – Will Brannon
            Jan 9 at 3:52




            $begingroup$
            But only in the case of an infinite state space, right? I was only talking about the finite-state case (and probably should have said that more clearly).
            $endgroup$
            – Will Brannon
            Jan 9 at 3:52












            $begingroup$
            Then you are describing (non-)communicability between states rather than transience.
            $endgroup$
            – Did
            Jan 9 at 6:48




            $begingroup$
            Then you are describing (non-)communicability between states rather than transience.
            $endgroup$
            – Did
            Jan 9 at 6:48


















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            0 1 What exactly does the error message beginning with E: mean? I assume it has to do with the file structure on Ubuntu-on-Windows, but what exactly? The following package was automatically installed and is no longer required: os-prober Use 'apt-get autoremove' to remove it. The following extra packages will be installed: libxslt1.1 The following NEW packages will be installed: libxslt1.1 xmlstarlet 0 upgraded, 2 newly installed, 0 to remove and 0 not upgraded. Need to get 435 kB of archives. After this operation, 1023 kB of additional disk space will be used. Get:1 http://archive.ubuntu.com/ubuntu/ trusty/main libxslt1.1 amd64 1.1.28-2build1 [145 kB] Get:2 http://archive.ubuntu.com/ubuntu/ trusty/universe xmlstarlet amd64 1.5.0-1 [290 kB] ...
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            Antiochus X Eusebes

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            Antiochus X Eusebes From Wikipedia, the free encyclopedia Jump to navigation Jump to search King of Syria Antiochus X Eusebes Antiochus X's portrait on the obverse of a tetradrachm King of Syria (Seleucid Empire) Reign 95–92 or 88 BC Predecessor Seleucus VI, Demetrius III Successor Demetrius III, Philip I Born c.  113 BC Died 92 or 88 BC Spouse Cleopatra Selene Issue Detail Antiochus XIII Dynasty Seleucid Father Antiochus IX Mother Cleopatra IV ? Antiochus X Eusebes Philopator (Greek: Ἀντίοχος Εὐσεβής Φιλοπάτωρ ; c.  113 –92 or 88 BC) was a Hellenistic Seleucid monarch who reigned as the King of Syria between 95 and 92 or 89/88 BC (224 Seleucid year (SE)). [note 1] He was the son of Antiochus IX and perhaps his Egyptian wife Cleopatra IV. He lived at a period that witnessed a general disintegration of Seleucid Syria characterized by civil wars, foreign inter...
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