What's the difference between the expectation of a function of a random variable and the law of the...












0












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Given a random variable $X$, some function $g(X)$, and $X$'s pdf $p_x(X)$ I know from probability
that:
$$mathbb{E}(g(X)) = int_x g(X)p_x(X) dx$$



In my reading, the Law of the Unconscious Statistician (LotUS) came up as a reason for one of steps of a proof in an academic paper. When I looked into the wiki link above, it seems to say the same thing as the equation above.



My question is, is there a difference between the two? Or is the LotUS just a formalism or a nickname for the expectation of a function of a random variable?










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  • $begingroup$
    Actually doesnt the wikipedia page you mentioned answer this? Maybe in the etymology section?
    $endgroup$
    – Ben
    Dec 27 '18 at 6:48
















0












$begingroup$


Given a random variable $X$, some function $g(X)$, and $X$'s pdf $p_x(X)$ I know from probability
that:
$$mathbb{E}(g(X)) = int_x g(X)p_x(X) dx$$



In my reading, the Law of the Unconscious Statistician (LotUS) came up as a reason for one of steps of a proof in an academic paper. When I looked into the wiki link above, it seems to say the same thing as the equation above.



My question is, is there a difference between the two? Or is the LotUS just a formalism or a nickname for the expectation of a function of a random variable?










share|cite|improve this question









$endgroup$












  • $begingroup$
    Actually doesnt the wikipedia page you mentioned answer this? Maybe in the etymology section?
    $endgroup$
    – Ben
    Dec 27 '18 at 6:48














0












0








0





$begingroup$


Given a random variable $X$, some function $g(X)$, and $X$'s pdf $p_x(X)$ I know from probability
that:
$$mathbb{E}(g(X)) = int_x g(X)p_x(X) dx$$



In my reading, the Law of the Unconscious Statistician (LotUS) came up as a reason for one of steps of a proof in an academic paper. When I looked into the wiki link above, it seems to say the same thing as the equation above.



My question is, is there a difference between the two? Or is the LotUS just a formalism or a nickname for the expectation of a function of a random variable?










share|cite|improve this question









$endgroup$




Given a random variable $X$, some function $g(X)$, and $X$'s pdf $p_x(X)$ I know from probability
that:
$$mathbb{E}(g(X)) = int_x g(X)p_x(X) dx$$



In my reading, the Law of the Unconscious Statistician (LotUS) came up as a reason for one of steps of a proof in an academic paper. When I looked into the wiki link above, it seems to say the same thing as the equation above.



My question is, is there a difference between the two? Or is the LotUS just a formalism or a nickname for the expectation of a function of a random variable?







statistics expected-value






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asked Dec 27 '18 at 6:38









RVCRVC

30818




30818












  • $begingroup$
    Actually doesnt the wikipedia page you mentioned answer this? Maybe in the etymology section?
    $endgroup$
    – Ben
    Dec 27 '18 at 6:48


















  • $begingroup$
    Actually doesnt the wikipedia page you mentioned answer this? Maybe in the etymology section?
    $endgroup$
    – Ben
    Dec 27 '18 at 6:48
















$begingroup$
Actually doesnt the wikipedia page you mentioned answer this? Maybe in the etymology section?
$endgroup$
– Ben
Dec 27 '18 at 6:48




$begingroup$
Actually doesnt the wikipedia page you mentioned answer this? Maybe in the etymology section?
$endgroup$
– Ben
Dec 27 '18 at 6:48










2 Answers
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$begingroup$

The "Law of the Unconscious Statistician" is just a name for the fact that $E(g(X))$ is given by the formula you wrote. There is no difference.






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    1












    $begingroup$

    Finding $mathbb Eg(X)$ can be done on several ways.



    One of them is $$mathbb Eg(X)=int y;dF_Y(y)$$where $Y:=g(X)$ and $F_Y$ denotes the CDF of $Y$.



    Another (in almost all cases more convenient) is practicizing LOTUS:$$mathbb Eg(X)=int g(x);dF_X(x)$$where $F_X$ denotes the CDF of $X$.



    This works if you know the distribution of $X$ and you can hold your shoulders for the distribution of $g(X)$.



    In both cases a PDF or PMF might exist leading to variants.



    One of them is mentioned by you:$$mathbb Eg(X)=int g(x)p_X(x);dx$$where $p_X$ denotes a PDF of $X$.






    share|cite|improve this answer









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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












      $begingroup$

      The "Law of the Unconscious Statistician" is just a name for the fact that $E(g(X))$ is given by the formula you wrote. There is no difference.






      share|cite|improve this answer









      $endgroup$


















        1












        $begingroup$

        The "Law of the Unconscious Statistician" is just a name for the fact that $E(g(X))$ is given by the formula you wrote. There is no difference.






        share|cite|improve this answer









        $endgroup$
















          1












          1








          1





          $begingroup$

          The "Law of the Unconscious Statistician" is just a name for the fact that $E(g(X))$ is given by the formula you wrote. There is no difference.






          share|cite|improve this answer









          $endgroup$



          The "Law of the Unconscious Statistician" is just a name for the fact that $E(g(X))$ is given by the formula you wrote. There is no difference.







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered Dec 27 '18 at 6:55









          littleOlittleO

          30.1k647110




          30.1k647110























              1












              $begingroup$

              Finding $mathbb Eg(X)$ can be done on several ways.



              One of them is $$mathbb Eg(X)=int y;dF_Y(y)$$where $Y:=g(X)$ and $F_Y$ denotes the CDF of $Y$.



              Another (in almost all cases more convenient) is practicizing LOTUS:$$mathbb Eg(X)=int g(x);dF_X(x)$$where $F_X$ denotes the CDF of $X$.



              This works if you know the distribution of $X$ and you can hold your shoulders for the distribution of $g(X)$.



              In both cases a PDF or PMF might exist leading to variants.



              One of them is mentioned by you:$$mathbb Eg(X)=int g(x)p_X(x);dx$$where $p_X$ denotes a PDF of $X$.






              share|cite|improve this answer









              $endgroup$


















                1












                $begingroup$

                Finding $mathbb Eg(X)$ can be done on several ways.



                One of them is $$mathbb Eg(X)=int y;dF_Y(y)$$where $Y:=g(X)$ and $F_Y$ denotes the CDF of $Y$.



                Another (in almost all cases more convenient) is practicizing LOTUS:$$mathbb Eg(X)=int g(x);dF_X(x)$$where $F_X$ denotes the CDF of $X$.



                This works if you know the distribution of $X$ and you can hold your shoulders for the distribution of $g(X)$.



                In both cases a PDF or PMF might exist leading to variants.



                One of them is mentioned by you:$$mathbb Eg(X)=int g(x)p_X(x);dx$$where $p_X$ denotes a PDF of $X$.






                share|cite|improve this answer









                $endgroup$
















                  1












                  1








                  1





                  $begingroup$

                  Finding $mathbb Eg(X)$ can be done on several ways.



                  One of them is $$mathbb Eg(X)=int y;dF_Y(y)$$where $Y:=g(X)$ and $F_Y$ denotes the CDF of $Y$.



                  Another (in almost all cases more convenient) is practicizing LOTUS:$$mathbb Eg(X)=int g(x);dF_X(x)$$where $F_X$ denotes the CDF of $X$.



                  This works if you know the distribution of $X$ and you can hold your shoulders for the distribution of $g(X)$.



                  In both cases a PDF or PMF might exist leading to variants.



                  One of them is mentioned by you:$$mathbb Eg(X)=int g(x)p_X(x);dx$$where $p_X$ denotes a PDF of $X$.






                  share|cite|improve this answer









                  $endgroup$



                  Finding $mathbb Eg(X)$ can be done on several ways.



                  One of them is $$mathbb Eg(X)=int y;dF_Y(y)$$where $Y:=g(X)$ and $F_Y$ denotes the CDF of $Y$.



                  Another (in almost all cases more convenient) is practicizing LOTUS:$$mathbb Eg(X)=int g(x);dF_X(x)$$where $F_X$ denotes the CDF of $X$.



                  This works if you know the distribution of $X$ and you can hold your shoulders for the distribution of $g(X)$.



                  In both cases a PDF or PMF might exist leading to variants.



                  One of them is mentioned by you:$$mathbb Eg(X)=int g(x)p_X(x);dx$$where $p_X$ denotes a PDF of $X$.







                  share|cite|improve this answer












                  share|cite|improve this answer



                  share|cite|improve this answer










                  answered Dec 27 '18 at 8:08









                  drhabdrhab

                  103k545136




                  103k545136






























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