Simple proof of a theorem on convergence of series











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Let $p_jge0, j=1,2,3,dots,$ and suppose $sum_j p_j=1.$ Is there a simple proof that $$sum_{j=1}^infty{jp_j}tag{1}$$ converges? My question arises from the answer to this question. Consider a Markov chain with state space ${1,2,3,dots}.$ If the chain is is state $1,$ it transitions to state $j$ with probability $p_j.$ If it is in state $j>1$ then it always transitions to state $j-1$. The chain is irreducible and aperiodic, so it has a unique stationary distribution. The sum $(1)$ arises in computing the stationary probabilities, so it must converge.



I've been trying unsuccessfully to find a more direct proof. There isn't any way to apply standard tests (root test, ratio test, Gauss's test) and I haven't any other ideas. (It's equivalent to the statement that if N is a random variable that takes positive integer values, then $E(N)$ exists, but I don't see how that helps. In fact, my intuition would be that this statement is false.)



EDIT



It has been amply shown that the statement is false. I would like to know the error in the linked question.










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  • 1




    Isn't this math.stackexchange.com/a/520619/42969 a counter-example?
    – Martin R
    Dec 5 at 16:24










  • You are right. What is the error in the linked question?
    – saulspatz
    Dec 5 at 16:27










  • I don't see an argument in the linked question for why this statement should be true, just a claim in a comment that "this problem shows" it to be true. Maybe if you provided more detail as to why you think the problems shows it to be true then someone could find your error.
    – MartianInvader
    Dec 5 at 17:52










  • @MartianInvader The stationary distribution exists by standard theorems on Markov chains, and the OP of the original question has shown how to calculate the the stationary probabilities. If my transformation of the series is correct, then it arises in the formula for those probabilities, and it must converge. So, it must be that the transformation is wrong, or there is an error in the original problem.
    – saulspatz
    Dec 5 at 17:56















up vote
5
down vote

favorite
2












Let $p_jge0, j=1,2,3,dots,$ and suppose $sum_j p_j=1.$ Is there a simple proof that $$sum_{j=1}^infty{jp_j}tag{1}$$ converges? My question arises from the answer to this question. Consider a Markov chain with state space ${1,2,3,dots}.$ If the chain is is state $1,$ it transitions to state $j$ with probability $p_j.$ If it is in state $j>1$ then it always transitions to state $j-1$. The chain is irreducible and aperiodic, so it has a unique stationary distribution. The sum $(1)$ arises in computing the stationary probabilities, so it must converge.



I've been trying unsuccessfully to find a more direct proof. There isn't any way to apply standard tests (root test, ratio test, Gauss's test) and I haven't any other ideas. (It's equivalent to the statement that if N is a random variable that takes positive integer values, then $E(N)$ exists, but I don't see how that helps. In fact, my intuition would be that this statement is false.)



EDIT



It has been amply shown that the statement is false. I would like to know the error in the linked question.










share|cite|improve this question




















  • 1




    Isn't this math.stackexchange.com/a/520619/42969 a counter-example?
    – Martin R
    Dec 5 at 16:24










  • You are right. What is the error in the linked question?
    – saulspatz
    Dec 5 at 16:27










  • I don't see an argument in the linked question for why this statement should be true, just a claim in a comment that "this problem shows" it to be true. Maybe if you provided more detail as to why you think the problems shows it to be true then someone could find your error.
    – MartianInvader
    Dec 5 at 17:52










  • @MartianInvader The stationary distribution exists by standard theorems on Markov chains, and the OP of the original question has shown how to calculate the the stationary probabilities. If my transformation of the series is correct, then it arises in the formula for those probabilities, and it must converge. So, it must be that the transformation is wrong, or there is an error in the original problem.
    – saulspatz
    Dec 5 at 17:56













up vote
5
down vote

favorite
2









up vote
5
down vote

favorite
2






2





Let $p_jge0, j=1,2,3,dots,$ and suppose $sum_j p_j=1.$ Is there a simple proof that $$sum_{j=1}^infty{jp_j}tag{1}$$ converges? My question arises from the answer to this question. Consider a Markov chain with state space ${1,2,3,dots}.$ If the chain is is state $1,$ it transitions to state $j$ with probability $p_j.$ If it is in state $j>1$ then it always transitions to state $j-1$. The chain is irreducible and aperiodic, so it has a unique stationary distribution. The sum $(1)$ arises in computing the stationary probabilities, so it must converge.



I've been trying unsuccessfully to find a more direct proof. There isn't any way to apply standard tests (root test, ratio test, Gauss's test) and I haven't any other ideas. (It's equivalent to the statement that if N is a random variable that takes positive integer values, then $E(N)$ exists, but I don't see how that helps. In fact, my intuition would be that this statement is false.)



EDIT



It has been amply shown that the statement is false. I would like to know the error in the linked question.










share|cite|improve this question















Let $p_jge0, j=1,2,3,dots,$ and suppose $sum_j p_j=1.$ Is there a simple proof that $$sum_{j=1}^infty{jp_j}tag{1}$$ converges? My question arises from the answer to this question. Consider a Markov chain with state space ${1,2,3,dots}.$ If the chain is is state $1,$ it transitions to state $j$ with probability $p_j.$ If it is in state $j>1$ then it always transitions to state $j-1$. The chain is irreducible and aperiodic, so it has a unique stationary distribution. The sum $(1)$ arises in computing the stationary probabilities, so it must converge.



I've been trying unsuccessfully to find a more direct proof. There isn't any way to apply standard tests (root test, ratio test, Gauss's test) and I haven't any other ideas. (It's equivalent to the statement that if N is a random variable that takes positive integer values, then $E(N)$ exists, but I don't see how that helps. In fact, my intuition would be that this statement is false.)



EDIT



It has been amply shown that the statement is false. I would like to know the error in the linked question.







probability sequences-and-series markov-chains






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edited Dec 5 at 16:33

























asked Dec 5 at 16:21









saulspatz

13.7k21328




13.7k21328








  • 1




    Isn't this math.stackexchange.com/a/520619/42969 a counter-example?
    – Martin R
    Dec 5 at 16:24










  • You are right. What is the error in the linked question?
    – saulspatz
    Dec 5 at 16:27










  • I don't see an argument in the linked question for why this statement should be true, just a claim in a comment that "this problem shows" it to be true. Maybe if you provided more detail as to why you think the problems shows it to be true then someone could find your error.
    – MartianInvader
    Dec 5 at 17:52










  • @MartianInvader The stationary distribution exists by standard theorems on Markov chains, and the OP of the original question has shown how to calculate the the stationary probabilities. If my transformation of the series is correct, then it arises in the formula for those probabilities, and it must converge. So, it must be that the transformation is wrong, or there is an error in the original problem.
    – saulspatz
    Dec 5 at 17:56














  • 1




    Isn't this math.stackexchange.com/a/520619/42969 a counter-example?
    – Martin R
    Dec 5 at 16:24










  • You are right. What is the error in the linked question?
    – saulspatz
    Dec 5 at 16:27










  • I don't see an argument in the linked question for why this statement should be true, just a claim in a comment that "this problem shows" it to be true. Maybe if you provided more detail as to why you think the problems shows it to be true then someone could find your error.
    – MartianInvader
    Dec 5 at 17:52










  • @MartianInvader The stationary distribution exists by standard theorems on Markov chains, and the OP of the original question has shown how to calculate the the stationary probabilities. If my transformation of the series is correct, then it arises in the formula for those probabilities, and it must converge. So, it must be that the transformation is wrong, or there is an error in the original problem.
    – saulspatz
    Dec 5 at 17:56








1




1




Isn't this math.stackexchange.com/a/520619/42969 a counter-example?
– Martin R
Dec 5 at 16:24




Isn't this math.stackexchange.com/a/520619/42969 a counter-example?
– Martin R
Dec 5 at 16:24












You are right. What is the error in the linked question?
– saulspatz
Dec 5 at 16:27




You are right. What is the error in the linked question?
– saulspatz
Dec 5 at 16:27












I don't see an argument in the linked question for why this statement should be true, just a claim in a comment that "this problem shows" it to be true. Maybe if you provided more detail as to why you think the problems shows it to be true then someone could find your error.
– MartianInvader
Dec 5 at 17:52




I don't see an argument in the linked question for why this statement should be true, just a claim in a comment that "this problem shows" it to be true. Maybe if you provided more detail as to why you think the problems shows it to be true then someone could find your error.
– MartianInvader
Dec 5 at 17:52












@MartianInvader The stationary distribution exists by standard theorems on Markov chains, and the OP of the original question has shown how to calculate the the stationary probabilities. If my transformation of the series is correct, then it arises in the formula for those probabilities, and it must converge. So, it must be that the transformation is wrong, or there is an error in the original problem.
– saulspatz
Dec 5 at 17:56




@MartianInvader The stationary distribution exists by standard theorems on Markov chains, and the OP of the original question has shown how to calculate the the stationary probabilities. If my transformation of the series is correct, then it arises in the formula for those probabilities, and it must converge. So, it must be that the transformation is wrong, or there is an error in the original problem.
– saulspatz
Dec 5 at 17:56










5 Answers
5






active

oldest

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up vote
7
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accepted










Not all aperiodic, irreducible Markov processes have a stationary distribution. This is only true for finite state spaces. For infinite spaces, you need the process to be positive recurrent, meaning the expected time to return to a state is finite. Here, starting from $1$, the expected time to return to $1$ is $sum jp_j$. Therefore, your proof goes in circles; in order for the process to have a stationary distribution, you need $sum jp_j<infty$, and in order to prove that, you use that the process has a stationary distribution.



When the list $(p_1,p_2,dots)$ has too fat a tail, the process will never settle, and instead become more diffuse as time goes on.






share|cite|improve this answer























  • Thank you. That is what I had forgotten.
    – saulspatz
    Dec 5 at 17:57


















up vote
8
down vote













The statement is false. Put
$$
p_j=frac{6}{pi^2}frac{1}{j^2}quad (jgeq 1)
$$

where the constant is for normalization. Let $N$ be distributed according to this pmf. Then
$$
sum_{j=1}^infty jp_j=EN=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=infty
$$






share|cite|improve this answer





















  • Can you find the error in the linked question?
    – saulspatz
    Dec 5 at 16:28


















up vote
6
down vote













We have the series $displaystylesum_{j=1}^infty frac{1}{j(j+1)} = 1$, but $displaystylesum_{j=1}^infty frac{1}{j+1}$ diverges, so your affirmation is false.






share|cite|improve this answer





















  • Can you find the error in the linked question?
    – saulspatz
    Dec 5 at 16:29


















up vote
3
down vote













It is known that $$sum_{j=1}^infty dfrac{1}{j^2}=dfrac{pi^2}{6}.$$



So if you take $p_j=frac{6}{(pi j)^2}$, you have $sum_{j=1}^infty p_j=1$ yet $sum_{j=1}^infty jp_j=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=+infty.$






share|cite|improve this answer





















  • Can you find the error in the linked question?
    – saulspatz
    Dec 5 at 16:28










  • @saulspatz Looked it up. Didn't find any mistake. Rather, for your answer to hold, you must show that $$lim_{kto +infty}sum_{j=k}^infty(j-k)p_j=0,$$ which is not obvious to me.
    – Scientifica
    Dec 5 at 17:10




















up vote
3
down vote













Another counterexample can be derived from the St. Petersburg paradox. Suppose that $p_j=frac1j$ if $j$ is a power of $2$, and $0$ otherwise. Then $sum p_j = sum 2^{-k}=1$, but $jp_j=1$ whenever $j$ is a power of $2$, and thus $sum jp_j$ diverges.






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    5 Answers
    5






    active

    oldest

    votes








    5 Answers
    5






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes








    up vote
    7
    down vote



    accepted










    Not all aperiodic, irreducible Markov processes have a stationary distribution. This is only true for finite state spaces. For infinite spaces, you need the process to be positive recurrent, meaning the expected time to return to a state is finite. Here, starting from $1$, the expected time to return to $1$ is $sum jp_j$. Therefore, your proof goes in circles; in order for the process to have a stationary distribution, you need $sum jp_j<infty$, and in order to prove that, you use that the process has a stationary distribution.



    When the list $(p_1,p_2,dots)$ has too fat a tail, the process will never settle, and instead become more diffuse as time goes on.






    share|cite|improve this answer























    • Thank you. That is what I had forgotten.
      – saulspatz
      Dec 5 at 17:57















    up vote
    7
    down vote



    accepted










    Not all aperiodic, irreducible Markov processes have a stationary distribution. This is only true for finite state spaces. For infinite spaces, you need the process to be positive recurrent, meaning the expected time to return to a state is finite. Here, starting from $1$, the expected time to return to $1$ is $sum jp_j$. Therefore, your proof goes in circles; in order for the process to have a stationary distribution, you need $sum jp_j<infty$, and in order to prove that, you use that the process has a stationary distribution.



    When the list $(p_1,p_2,dots)$ has too fat a tail, the process will never settle, and instead become more diffuse as time goes on.






    share|cite|improve this answer























    • Thank you. That is what I had forgotten.
      – saulspatz
      Dec 5 at 17:57













    up vote
    7
    down vote



    accepted







    up vote
    7
    down vote



    accepted






    Not all aperiodic, irreducible Markov processes have a stationary distribution. This is only true for finite state spaces. For infinite spaces, you need the process to be positive recurrent, meaning the expected time to return to a state is finite. Here, starting from $1$, the expected time to return to $1$ is $sum jp_j$. Therefore, your proof goes in circles; in order for the process to have a stationary distribution, you need $sum jp_j<infty$, and in order to prove that, you use that the process has a stationary distribution.



    When the list $(p_1,p_2,dots)$ has too fat a tail, the process will never settle, and instead become more diffuse as time goes on.






    share|cite|improve this answer














    Not all aperiodic, irreducible Markov processes have a stationary distribution. This is only true for finite state spaces. For infinite spaces, you need the process to be positive recurrent, meaning the expected time to return to a state is finite. Here, starting from $1$, the expected time to return to $1$ is $sum jp_j$. Therefore, your proof goes in circles; in order for the process to have a stationary distribution, you need $sum jp_j<infty$, and in order to prove that, you use that the process has a stationary distribution.



    When the list $(p_1,p_2,dots)$ has too fat a tail, the process will never settle, and instead become more diffuse as time goes on.







    share|cite|improve this answer














    share|cite|improve this answer



    share|cite|improve this answer








    edited Dec 5 at 17:58

























    answered Dec 5 at 17:55









    Mike Earnest

    19.6k11950




    19.6k11950












    • Thank you. That is what I had forgotten.
      – saulspatz
      Dec 5 at 17:57


















    • Thank you. That is what I had forgotten.
      – saulspatz
      Dec 5 at 17:57
















    Thank you. That is what I had forgotten.
    – saulspatz
    Dec 5 at 17:57




    Thank you. That is what I had forgotten.
    – saulspatz
    Dec 5 at 17:57










    up vote
    8
    down vote













    The statement is false. Put
    $$
    p_j=frac{6}{pi^2}frac{1}{j^2}quad (jgeq 1)
    $$

    where the constant is for normalization. Let $N$ be distributed according to this pmf. Then
    $$
    sum_{j=1}^infty jp_j=EN=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=infty
    $$






    share|cite|improve this answer





















    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:28















    up vote
    8
    down vote













    The statement is false. Put
    $$
    p_j=frac{6}{pi^2}frac{1}{j^2}quad (jgeq 1)
    $$

    where the constant is for normalization. Let $N$ be distributed according to this pmf. Then
    $$
    sum_{j=1}^infty jp_j=EN=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=infty
    $$






    share|cite|improve this answer





















    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:28













    up vote
    8
    down vote










    up vote
    8
    down vote









    The statement is false. Put
    $$
    p_j=frac{6}{pi^2}frac{1}{j^2}quad (jgeq 1)
    $$

    where the constant is for normalization. Let $N$ be distributed according to this pmf. Then
    $$
    sum_{j=1}^infty jp_j=EN=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=infty
    $$






    share|cite|improve this answer












    The statement is false. Put
    $$
    p_j=frac{6}{pi^2}frac{1}{j^2}quad (jgeq 1)
    $$

    where the constant is for normalization. Let $N$ be distributed according to this pmf. Then
    $$
    sum_{j=1}^infty jp_j=EN=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=infty
    $$







    share|cite|improve this answer












    share|cite|improve this answer



    share|cite|improve this answer










    answered Dec 5 at 16:26









    Foobaz John

    20.3k41250




    20.3k41250












    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:28


















    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:28
















    Can you find the error in the linked question?
    – saulspatz
    Dec 5 at 16:28




    Can you find the error in the linked question?
    – saulspatz
    Dec 5 at 16:28










    up vote
    6
    down vote













    We have the series $displaystylesum_{j=1}^infty frac{1}{j(j+1)} = 1$, but $displaystylesum_{j=1}^infty frac{1}{j+1}$ diverges, so your affirmation is false.






    share|cite|improve this answer





















    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:29















    up vote
    6
    down vote













    We have the series $displaystylesum_{j=1}^infty frac{1}{j(j+1)} = 1$, but $displaystylesum_{j=1}^infty frac{1}{j+1}$ diverges, so your affirmation is false.






    share|cite|improve this answer





















    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:29













    up vote
    6
    down vote










    up vote
    6
    down vote









    We have the series $displaystylesum_{j=1}^infty frac{1}{j(j+1)} = 1$, but $displaystylesum_{j=1}^infty frac{1}{j+1}$ diverges, so your affirmation is false.






    share|cite|improve this answer












    We have the series $displaystylesum_{j=1}^infty frac{1}{j(j+1)} = 1$, but $displaystylesum_{j=1}^infty frac{1}{j+1}$ diverges, so your affirmation is false.







    share|cite|improve this answer












    share|cite|improve this answer



    share|cite|improve this answer










    answered Dec 5 at 16:28









    jjagmath

    2206




    2206












    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:29


















    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:29
















    Can you find the error in the linked question?
    – saulspatz
    Dec 5 at 16:29




    Can you find the error in the linked question?
    – saulspatz
    Dec 5 at 16:29










    up vote
    3
    down vote













    It is known that $$sum_{j=1}^infty dfrac{1}{j^2}=dfrac{pi^2}{6}.$$



    So if you take $p_j=frac{6}{(pi j)^2}$, you have $sum_{j=1}^infty p_j=1$ yet $sum_{j=1}^infty jp_j=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=+infty.$






    share|cite|improve this answer





















    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:28










    • @saulspatz Looked it up. Didn't find any mistake. Rather, for your answer to hold, you must show that $$lim_{kto +infty}sum_{j=k}^infty(j-k)p_j=0,$$ which is not obvious to me.
      – Scientifica
      Dec 5 at 17:10

















    up vote
    3
    down vote













    It is known that $$sum_{j=1}^infty dfrac{1}{j^2}=dfrac{pi^2}{6}.$$



    So if you take $p_j=frac{6}{(pi j)^2}$, you have $sum_{j=1}^infty p_j=1$ yet $sum_{j=1}^infty jp_j=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=+infty.$






    share|cite|improve this answer





















    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:28










    • @saulspatz Looked it up. Didn't find any mistake. Rather, for your answer to hold, you must show that $$lim_{kto +infty}sum_{j=k}^infty(j-k)p_j=0,$$ which is not obvious to me.
      – Scientifica
      Dec 5 at 17:10















    up vote
    3
    down vote










    up vote
    3
    down vote









    It is known that $$sum_{j=1}^infty dfrac{1}{j^2}=dfrac{pi^2}{6}.$$



    So if you take $p_j=frac{6}{(pi j)^2}$, you have $sum_{j=1}^infty p_j=1$ yet $sum_{j=1}^infty jp_j=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=+infty.$






    share|cite|improve this answer












    It is known that $$sum_{j=1}^infty dfrac{1}{j^2}=dfrac{pi^2}{6}.$$



    So if you take $p_j=frac{6}{(pi j)^2}$, you have $sum_{j=1}^infty p_j=1$ yet $sum_{j=1}^infty jp_j=frac{6}{pi^2}sum_{j=1}^inftyfrac{1}{j}=+infty.$







    share|cite|improve this answer












    share|cite|improve this answer



    share|cite|improve this answer










    answered Dec 5 at 16:25









    Scientifica

    6,28641333




    6,28641333












    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:28










    • @saulspatz Looked it up. Didn't find any mistake. Rather, for your answer to hold, you must show that $$lim_{kto +infty}sum_{j=k}^infty(j-k)p_j=0,$$ which is not obvious to me.
      – Scientifica
      Dec 5 at 17:10




















    • Can you find the error in the linked question?
      – saulspatz
      Dec 5 at 16:28










    • @saulspatz Looked it up. Didn't find any mistake. Rather, for your answer to hold, you must show that $$lim_{kto +infty}sum_{j=k}^infty(j-k)p_j=0,$$ which is not obvious to me.
      – Scientifica
      Dec 5 at 17:10


















    Can you find the error in the linked question?
    – saulspatz
    Dec 5 at 16:28




    Can you find the error in the linked question?
    – saulspatz
    Dec 5 at 16:28












    @saulspatz Looked it up. Didn't find any mistake. Rather, for your answer to hold, you must show that $$lim_{kto +infty}sum_{j=k}^infty(j-k)p_j=0,$$ which is not obvious to me.
    – Scientifica
    Dec 5 at 17:10






    @saulspatz Looked it up. Didn't find any mistake. Rather, for your answer to hold, you must show that $$lim_{kto +infty}sum_{j=k}^infty(j-k)p_j=0,$$ which is not obvious to me.
    – Scientifica
    Dec 5 at 17:10












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    Another counterexample can be derived from the St. Petersburg paradox. Suppose that $p_j=frac1j$ if $j$ is a power of $2$, and $0$ otherwise. Then $sum p_j = sum 2^{-k}=1$, but $jp_j=1$ whenever $j$ is a power of $2$, and thus $sum jp_j$ diverges.






    share|cite|improve this answer

























      up vote
      3
      down vote













      Another counterexample can be derived from the St. Petersburg paradox. Suppose that $p_j=frac1j$ if $j$ is a power of $2$, and $0$ otherwise. Then $sum p_j = sum 2^{-k}=1$, but $jp_j=1$ whenever $j$ is a power of $2$, and thus $sum jp_j$ diverges.






      share|cite|improve this answer























        up vote
        3
        down vote










        up vote
        3
        down vote









        Another counterexample can be derived from the St. Petersburg paradox. Suppose that $p_j=frac1j$ if $j$ is a power of $2$, and $0$ otherwise. Then $sum p_j = sum 2^{-k}=1$, but $jp_j=1$ whenever $j$ is a power of $2$, and thus $sum jp_j$ diverges.






        share|cite|improve this answer












        Another counterexample can be derived from the St. Petersburg paradox. Suppose that $p_j=frac1j$ if $j$ is a power of $2$, and $0$ otherwise. Then $sum p_j = sum 2^{-k}=1$, but $jp_j=1$ whenever $j$ is a power of $2$, and thus $sum jp_j$ diverges.







        share|cite|improve this answer












        share|cite|improve this answer



        share|cite|improve this answer










        answered Dec 5 at 18:24









        Acccumulation

        6,6112616




        6,6112616






























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