Estimate relationship between two Bernoulli random variables












2












$begingroup$




  • $X$ and $Y$ are Bernoulli random variables


  • $X$ and $Y$ are not independent


  • $x_{t} = P(X_t = 1)$ and $y_{t} = P(X_t = 1)$ for time $t$.

  • Is it possible to estimate $P(Y = 1 | X = 1)$ from many pairs of $x_{t}$ and $y_{t}$?


I tried:



$$frac{sum_{t}y_{t}x_{t}}{sum_{t}x_{t}}$$



But this formula doesn't make sense because that means $P(X = 1|X = 1)$ is



$$frac{sum_{t}x_{t}x_{t}}{sum_{t}x_{t}}$$



which is not necessarily 1.










share|cite|improve this question











$endgroup$












  • $begingroup$
    seems related to math.stackexchange.com/questions/610443/…
    $endgroup$
    – R zu
    Dec 12 '18 at 17:00










  • $begingroup$
    Can't do much in this case. Have to assume that the two probability variables are independent at each point of time.
    $endgroup$
    – R zu
    Dec 12 '18 at 18:39










  • $begingroup$
    How do you get $x_t$ and $y_t$? Are they given? Are ${(X_t,Y_t)}$ i.i.d?
    $endgroup$
    – d.k.o.
    Dec 12 '18 at 19:24










  • $begingroup$
    Something here doesn't make too much sense; what is $(X_t,Y_t)$? It seems like you want $(X_t,Y_t)_{t}$ to be independent samples of $(X,Y)$, but their parameters seem to be $(x_t,y_t)$, which further seem to be changing.
    $endgroup$
    – Marcus M
    Dec 12 '18 at 19:25
















2












$begingroup$




  • $X$ and $Y$ are Bernoulli random variables


  • $X$ and $Y$ are not independent


  • $x_{t} = P(X_t = 1)$ and $y_{t} = P(X_t = 1)$ for time $t$.

  • Is it possible to estimate $P(Y = 1 | X = 1)$ from many pairs of $x_{t}$ and $y_{t}$?


I tried:



$$frac{sum_{t}y_{t}x_{t}}{sum_{t}x_{t}}$$



But this formula doesn't make sense because that means $P(X = 1|X = 1)$ is



$$frac{sum_{t}x_{t}x_{t}}{sum_{t}x_{t}}$$



which is not necessarily 1.










share|cite|improve this question











$endgroup$












  • $begingroup$
    seems related to math.stackexchange.com/questions/610443/…
    $endgroup$
    – R zu
    Dec 12 '18 at 17:00










  • $begingroup$
    Can't do much in this case. Have to assume that the two probability variables are independent at each point of time.
    $endgroup$
    – R zu
    Dec 12 '18 at 18:39










  • $begingroup$
    How do you get $x_t$ and $y_t$? Are they given? Are ${(X_t,Y_t)}$ i.i.d?
    $endgroup$
    – d.k.o.
    Dec 12 '18 at 19:24










  • $begingroup$
    Something here doesn't make too much sense; what is $(X_t,Y_t)$? It seems like you want $(X_t,Y_t)_{t}$ to be independent samples of $(X,Y)$, but their parameters seem to be $(x_t,y_t)$, which further seem to be changing.
    $endgroup$
    – Marcus M
    Dec 12 '18 at 19:25














2












2








2





$begingroup$




  • $X$ and $Y$ are Bernoulli random variables


  • $X$ and $Y$ are not independent


  • $x_{t} = P(X_t = 1)$ and $y_{t} = P(X_t = 1)$ for time $t$.

  • Is it possible to estimate $P(Y = 1 | X = 1)$ from many pairs of $x_{t}$ and $y_{t}$?


I tried:



$$frac{sum_{t}y_{t}x_{t}}{sum_{t}x_{t}}$$



But this formula doesn't make sense because that means $P(X = 1|X = 1)$ is



$$frac{sum_{t}x_{t}x_{t}}{sum_{t}x_{t}}$$



which is not necessarily 1.










share|cite|improve this question











$endgroup$






  • $X$ and $Y$ are Bernoulli random variables


  • $X$ and $Y$ are not independent


  • $x_{t} = P(X_t = 1)$ and $y_{t} = P(X_t = 1)$ for time $t$.

  • Is it possible to estimate $P(Y = 1 | X = 1)$ from many pairs of $x_{t}$ and $y_{t}$?


I tried:



$$frac{sum_{t}y_{t}x_{t}}{sum_{t}x_{t}}$$



But this formula doesn't make sense because that means $P(X = 1|X = 1)$ is



$$frac{sum_{t}x_{t}x_{t}}{sum_{t}x_{t}}$$



which is not necessarily 1.







probability probability-theory parameter-estimation






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Dec 12 '18 at 17:23







R zu

















asked Dec 12 '18 at 16:49









R zuR zu

33910




33910












  • $begingroup$
    seems related to math.stackexchange.com/questions/610443/…
    $endgroup$
    – R zu
    Dec 12 '18 at 17:00










  • $begingroup$
    Can't do much in this case. Have to assume that the two probability variables are independent at each point of time.
    $endgroup$
    – R zu
    Dec 12 '18 at 18:39










  • $begingroup$
    How do you get $x_t$ and $y_t$? Are they given? Are ${(X_t,Y_t)}$ i.i.d?
    $endgroup$
    – d.k.o.
    Dec 12 '18 at 19:24










  • $begingroup$
    Something here doesn't make too much sense; what is $(X_t,Y_t)$? It seems like you want $(X_t,Y_t)_{t}$ to be independent samples of $(X,Y)$, but their parameters seem to be $(x_t,y_t)$, which further seem to be changing.
    $endgroup$
    – Marcus M
    Dec 12 '18 at 19:25


















  • $begingroup$
    seems related to math.stackexchange.com/questions/610443/…
    $endgroup$
    – R zu
    Dec 12 '18 at 17:00










  • $begingroup$
    Can't do much in this case. Have to assume that the two probability variables are independent at each point of time.
    $endgroup$
    – R zu
    Dec 12 '18 at 18:39










  • $begingroup$
    How do you get $x_t$ and $y_t$? Are they given? Are ${(X_t,Y_t)}$ i.i.d?
    $endgroup$
    – d.k.o.
    Dec 12 '18 at 19:24










  • $begingroup$
    Something here doesn't make too much sense; what is $(X_t,Y_t)$? It seems like you want $(X_t,Y_t)_{t}$ to be independent samples of $(X,Y)$, but their parameters seem to be $(x_t,y_t)$, which further seem to be changing.
    $endgroup$
    – Marcus M
    Dec 12 '18 at 19:25
















$begingroup$
seems related to math.stackexchange.com/questions/610443/…
$endgroup$
– R zu
Dec 12 '18 at 17:00




$begingroup$
seems related to math.stackexchange.com/questions/610443/…
$endgroup$
– R zu
Dec 12 '18 at 17:00












$begingroup$
Can't do much in this case. Have to assume that the two probability variables are independent at each point of time.
$endgroup$
– R zu
Dec 12 '18 at 18:39




$begingroup$
Can't do much in this case. Have to assume that the two probability variables are independent at each point of time.
$endgroup$
– R zu
Dec 12 '18 at 18:39












$begingroup$
How do you get $x_t$ and $y_t$? Are they given? Are ${(X_t,Y_t)}$ i.i.d?
$endgroup$
– d.k.o.
Dec 12 '18 at 19:24




$begingroup$
How do you get $x_t$ and $y_t$? Are they given? Are ${(X_t,Y_t)}$ i.i.d?
$endgroup$
– d.k.o.
Dec 12 '18 at 19:24












$begingroup$
Something here doesn't make too much sense; what is $(X_t,Y_t)$? It seems like you want $(X_t,Y_t)_{t}$ to be independent samples of $(X,Y)$, but their parameters seem to be $(x_t,y_t)$, which further seem to be changing.
$endgroup$
– Marcus M
Dec 12 '18 at 19:25




$begingroup$
Something here doesn't make too much sense; what is $(X_t,Y_t)$? It seems like you want $(X_t,Y_t)_{t}$ to be independent samples of $(X,Y)$, but their parameters seem to be $(x_t,y_t)$, which further seem to be changing.
$endgroup$
– Marcus M
Dec 12 '18 at 19:25










1 Answer
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Throw away the data where $x_t neq 1$. Then compute the proportion of the remaining data where $y_t = 1$.






share|cite|improve this answer









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    1 Answer
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    1 Answer
    1






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    $begingroup$

    Throw away the data where $x_t neq 1$. Then compute the proportion of the remaining data where $y_t = 1$.






    share|cite|improve this answer









    $endgroup$


















      1












      $begingroup$

      Throw away the data where $x_t neq 1$. Then compute the proportion of the remaining data where $y_t = 1$.






      share|cite|improve this answer









      $endgroup$
















        1












        1








        1





        $begingroup$

        Throw away the data where $x_t neq 1$. Then compute the proportion of the remaining data where $y_t = 1$.






        share|cite|improve this answer









        $endgroup$



        Throw away the data where $x_t neq 1$. Then compute the proportion of the remaining data where $y_t = 1$.







        share|cite|improve this answer












        share|cite|improve this answer



        share|cite|improve this answer










        answered Dec 13 '18 at 3:39









        hunterhunter

        15.5k32640




        15.5k32640






























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