States of Markov chain and stationary distribution












-1














Let $X$ be a Markov chain with a state space $S={{0,1,2,... }}$ and a transition matrix $P$ with given $p_{i,0}=frac{i}{i+1}$ and $p_{i,i+1}=frac{1}{i+1}$, for $i=0,1,2,...$. Find out which states are transient, null and not-null. Find stationary distribution.



I have that $p_{0,0}=0, p_{1,0}=frac{1}{2}, p_{2,0}=frac{2}{3}, p_{3,0}=frac{3}{4}$ and $p_{0,1}=1, p_{1,2}=frac{1}{2}, p_{2,3}=frac{1}{3}, p_{3,4}=frac{1}{4}$.



I think that all states are persistent, so no states are transient.



There is a theorem which says that a state is null $iff lim_{n to infty} p_{ii}(n)=0$, so state $0$ is null. I guess all the other states are non-null but I don't know how to prove it.
Also I don't know how to find stationary distribution.



Please help me with the above exercise, correct me where I'm wrong, and send any tips to the rest. Any will be much appreciated.






probability-theory stochastic-processes markov-chains







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  • "so state 0 is null" How do you know? Did you prove that $limlimits_{n to infty} p_{00}(n)=0$? How?
    – Did
    Jan 6 at 12:46










  • I just assumed that if $p_{0,0}=0$, then $lim_{n to infty}p_{0,0}(n)=0$, I know it might be naive, but I'm not aware how to approach this problem.
    – MacAbra
    Jan 6 at 13:10










  • Not especially naive, but squarely wrong because obviously absurd, right? Say, do you have any serious attempt to present?
    – Did
    Jan 6 at 13:21
















-1














Let $X$ be a Markov chain with a state space $S={{0,1,2,... }}$ and a transition matrix $P$ with given $p_{i,0}=frac{i}{i+1}$ and $p_{i,i+1}=frac{1}{i+1}$, for $i=0,1,2,...$. Find out which states are transient, null and not-null. Find stationary distribution.



I have that $p_{0,0}=0, p_{1,0}=frac{1}{2}, p_{2,0}=frac{2}{3}, p_{3,0}=frac{3}{4}$ and $p_{0,1}=1, p_{1,2}=frac{1}{2}, p_{2,3}=frac{1}{3}, p_{3,4}=frac{1}{4}$.



I think that all states are persistent, so no states are transient.



There is a theorem which says that a state is null $iff lim_{n to infty} p_{ii}(n)=0$, so state $0$ is null. I guess all the other states are non-null but I don't know how to prove it.
Also I don't know how to find stationary distribution.



Please help me with the above exercise, correct me where I'm wrong, and send any tips to the rest. Any will be much appreciated.






probability-theory stochastic-processes markov-chains







share|cite|improve this question






















  • "so state 0 is null" How do you know? Did you prove that $limlimits_{n to infty} p_{00}(n)=0$? How?
    – Did
    Jan 6 at 12:46










  • I just assumed that if $p_{0,0}=0$, then $lim_{n to infty}p_{0,0}(n)=0$, I know it might be naive, but I'm not aware how to approach this problem.
    – MacAbra
    Jan 6 at 13:10










  • Not especially naive, but squarely wrong because obviously absurd, right? Say, do you have any serious attempt to present?
    – Did
    Jan 6 at 13:21














-1












-1








-1







Let $X$ be a Markov chain with a state space $S={{0,1,2,... }}$ and a transition matrix $P$ with given $p_{i,0}=frac{i}{i+1}$ and $p_{i,i+1}=frac{1}{i+1}$, for $i=0,1,2,...$. Find out which states are transient, null and not-null. Find stationary distribution.



I have that $p_{0,0}=0, p_{1,0}=frac{1}{2}, p_{2,0}=frac{2}{3}, p_{3,0}=frac{3}{4}$ and $p_{0,1}=1, p_{1,2}=frac{1}{2}, p_{2,3}=frac{1}{3}, p_{3,4}=frac{1}{4}$.



I think that all states are persistent, so no states are transient.



There is a theorem which says that a state is null $iff lim_{n to infty} p_{ii}(n)=0$, so state $0$ is null. I guess all the other states are non-null but I don't know how to prove it.
Also I don't know how to find stationary distribution.



Please help me with the above exercise, correct me where I'm wrong, and send any tips to the rest. Any will be much appreciated.






probability-theory stochastic-processes markov-chains







share|cite|improve this question













Let $X$ be a Markov chain with a state space $S={{0,1,2,... }}$ and a transition matrix $P$ with given $p_{i,0}=frac{i}{i+1}$ and $p_{i,i+1}=frac{1}{i+1}$, for $i=0,1,2,...$. Find out which states are transient, null and not-null. Find stationary distribution.



I have that $p_{0,0}=0, p_{1,0}=frac{1}{2}, p_{2,0}=frac{2}{3}, p_{3,0}=frac{3}{4}$ and $p_{0,1}=1, p_{1,2}=frac{1}{2}, p_{2,3}=frac{1}{3}, p_{3,4}=frac{1}{4}$.



I think that all states are persistent, so no states are transient.



There is a theorem which says that a state is null $iff lim_{n to infty} p_{ii}(n)=0$, so state $0$ is null. I guess all the other states are non-null but I don't know how to prove it.
Also I don't know how to find stationary distribution.



Please help me with the above exercise, correct me where I'm wrong, and send any tips to the rest. Any will be much appreciated.






probability-theory stochastic-processes markov-chains




probability-theory stochastic-processes markov-chains






share|cite|improve this question













share|cite|improve this question











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asked Jan 6 at 12:04









MacAbraMacAbra

17019




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  • "so state 0 is null" How do you know? Did you prove that $limlimits_{n to infty} p_{00}(n)=0$? How?
    – Did
    Jan 6 at 12:46










  • I just assumed that if $p_{0,0}=0$, then $lim_{n to infty}p_{0,0}(n)=0$, I know it might be naive, but I'm not aware how to approach this problem.
    – MacAbra
    Jan 6 at 13:10










  • Not especially naive, but squarely wrong because obviously absurd, right? Say, do you have any serious attempt to present?
    – Did
    Jan 6 at 13:21


















  • "so state 0 is null" How do you know? Did you prove that $limlimits_{n to infty} p_{00}(n)=0$? How?
    – Did
    Jan 6 at 12:46










  • I just assumed that if $p_{0,0}=0$, then $lim_{n to infty}p_{0,0}(n)=0$, I know it might be naive, but I'm not aware how to approach this problem.
    – MacAbra
    Jan 6 at 13:10










  • Not especially naive, but squarely wrong because obviously absurd, right? Say, do you have any serious attempt to present?
    – Did
    Jan 6 at 13:21
















"so state 0 is null" How do you know? Did you prove that $limlimits_{n to infty} p_{00}(n)=0$? How?
– Did
Jan 6 at 12:46




"so state 0 is null" How do you know? Did you prove that $limlimits_{n to infty} p_{00}(n)=0$? How?
– Did
Jan 6 at 12:46












I just assumed that if $p_{0,0}=0$, then $lim_{n to infty}p_{0,0}(n)=0$, I know it might be naive, but I'm not aware how to approach this problem.
– MacAbra
Jan 6 at 13:10




I just assumed that if $p_{0,0}=0$, then $lim_{n to infty}p_{0,0}(n)=0$, I know it might be naive, but I'm not aware how to approach this problem.
– MacAbra
Jan 6 at 13:10












Not especially naive, but squarely wrong because obviously absurd, right? Say, do you have any serious attempt to present?
– Did
Jan 6 at 13:21




Not especially naive, but squarely wrong because obviously absurd, right? Say, do you have any serious attempt to present?
– Did
Jan 6 at 13:21










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You need to realise that since $p_{0,1} = 1$ and $p_{i,0} + p_{i,i+1} = 1$ then $p_{0,j}$ must be $0$ for all $j ne 1$ and $p_{i,k}$ must be $0$ for all $k ne 0 mbox{ or } i+1$ .



Let $pi_j = frac{e^{-1}}{j!}$ for all $j ge 0$. Then $pi_j$ is the stationary distribution because



begin{eqnarray}
sum_{i=0}^infty pi_i &=& 1 ,\
sum_{i=0}^infty pi_i p_{i,j} &=& frac{1}{j} pi_{j-1} = pi_j mbox{ for $j ge 1$, and}\
sum_{i=0}^infty pi_i p_{i,0}
&=& sum_{i=0}^infty frac{i}{i+1} pi_i \
&=& sum_{i=0}^infty left(1 - frac{1}{(i+1)}right)pi_i\
&=& e^{-1}sum_{i=0}^infty left(frac{1}{i!} - frac{1}{(i+1)!}right)\
&=& pi_0 .
end{eqnarray}
Consequently, all states are positive recurrent—none are transient or null recurrent.






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    You need to realise that since $p_{0,1} = 1$ and $p_{i,0} + p_{i,i+1} = 1$ then $p_{0,j}$ must be $0$ for all $j ne 1$ and $p_{i,k}$ must be $0$ for all $k ne 0 mbox{ or } i+1$ .



    Let $pi_j = frac{e^{-1}}{j!}$ for all $j ge 0$. Then $pi_j$ is the stationary distribution because



    begin{eqnarray}
    sum_{i=0}^infty pi_i &=& 1 ,\
    sum_{i=0}^infty pi_i p_{i,j} &=& frac{1}{j} pi_{j-1} = pi_j mbox{ for $j ge 1$, and}\
    sum_{i=0}^infty pi_i p_{i,0}
    &=& sum_{i=0}^infty frac{i}{i+1} pi_i \
    &=& sum_{i=0}^infty left(1 - frac{1}{(i+1)}right)pi_i\
    &=& e^{-1}sum_{i=0}^infty left(frac{1}{i!} - frac{1}{(i+1)!}right)\
    &=& pi_0 .
    end{eqnarray}
    Consequently, all states are positive recurrent—none are transient or null recurrent.






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      You need to realise that since $p_{0,1} = 1$ and $p_{i,0} + p_{i,i+1} = 1$ then $p_{0,j}$ must be $0$ for all $j ne 1$ and $p_{i,k}$ must be $0$ for all $k ne 0 mbox{ or } i+1$ .



      Let $pi_j = frac{e^{-1}}{j!}$ for all $j ge 0$. Then $pi_j$ is the stationary distribution because



      begin{eqnarray}
      sum_{i=0}^infty pi_i &=& 1 ,\
      sum_{i=0}^infty pi_i p_{i,j} &=& frac{1}{j} pi_{j-1} = pi_j mbox{ for $j ge 1$, and}\
      sum_{i=0}^infty pi_i p_{i,0}
      &=& sum_{i=0}^infty frac{i}{i+1} pi_i \
      &=& sum_{i=0}^infty left(1 - frac{1}{(i+1)}right)pi_i\
      &=& e^{-1}sum_{i=0}^infty left(frac{1}{i!} - frac{1}{(i+1)!}right)\
      &=& pi_0 .
      end{eqnarray}
      Consequently, all states are positive recurrent—none are transient or null recurrent.






      share|cite|improve this answer










      New contributor




      lonza leggiera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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        You need to realise that since $p_{0,1} = 1$ and $p_{i,0} + p_{i,i+1} = 1$ then $p_{0,j}$ must be $0$ for all $j ne 1$ and $p_{i,k}$ must be $0$ for all $k ne 0 mbox{ or } i+1$ .



        Let $pi_j = frac{e^{-1}}{j!}$ for all $j ge 0$. Then $pi_j$ is the stationary distribution because



        begin{eqnarray}
        sum_{i=0}^infty pi_i &=& 1 ,\
        sum_{i=0}^infty pi_i p_{i,j} &=& frac{1}{j} pi_{j-1} = pi_j mbox{ for $j ge 1$, and}\
        sum_{i=0}^infty pi_i p_{i,0}
        &=& sum_{i=0}^infty frac{i}{i+1} pi_i \
        &=& sum_{i=0}^infty left(1 - frac{1}{(i+1)}right)pi_i\
        &=& e^{-1}sum_{i=0}^infty left(frac{1}{i!} - frac{1}{(i+1)!}right)\
        &=& pi_0 .
        end{eqnarray}
        Consequently, all states are positive recurrent—none are transient or null recurrent.






        share|cite|improve this answer










        New contributor




        lonza leggiera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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        You need to realise that since $p_{0,1} = 1$ and $p_{i,0} + p_{i,i+1} = 1$ then $p_{0,j}$ must be $0$ for all $j ne 1$ and $p_{i,k}$ must be $0$ for all $k ne 0 mbox{ or } i+1$ .



        Let $pi_j = frac{e^{-1}}{j!}$ for all $j ge 0$. Then $pi_j$ is the stationary distribution because



        begin{eqnarray}
        sum_{i=0}^infty pi_i &=& 1 ,\
        sum_{i=0}^infty pi_i p_{i,j} &=& frac{1}{j} pi_{j-1} = pi_j mbox{ for $j ge 1$, and}\
        sum_{i=0}^infty pi_i p_{i,0}
        &=& sum_{i=0}^infty frac{i}{i+1} pi_i \
        &=& sum_{i=0}^infty left(1 - frac{1}{(i+1)}right)pi_i\
        &=& e^{-1}sum_{i=0}^infty left(frac{1}{i!} - frac{1}{(i+1)!}right)\
        &=& pi_0 .
        end{eqnarray}
        Consequently, all states are positive recurrent—none are transient or null recurrent.







        share|cite|improve this answer










        New contributor




        lonza leggiera is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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        share|cite|improve this answer



        share|cite|improve this answer








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        answered Jan 7 at 6:29









        lonza leggieralonza leggiera

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