How to read equation for summing probabilities of each member in a set?












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I’m reading through a chapter in https://www.probabilitycourse.com



There is a section ...




Consider a discrete random variable X with range $Range(X)=R_X$. Note that by definition the PMF is a probability measure, so it satisfies all properties of a probability measure. In particular, we have





  • $0 leq P_X(x) leq 1$ for all $x$, and

  • $Sigma_{ x in R_X}P_X(x)=1$




How do I read the bottom equation? Is it ...



Sum of all the probabilities of X for each x in $R_X$










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    0












    $begingroup$


    I’m reading through a chapter in https://www.probabilitycourse.com



    There is a section ...




    Consider a discrete random variable X with range $Range(X)=R_X$. Note that by definition the PMF is a probability measure, so it satisfies all properties of a probability measure. In particular, we have





    • $0 leq P_X(x) leq 1$ for all $x$, and

    • $Sigma_{ x in R_X}P_X(x)=1$




    How do I read the bottom equation? Is it ...



    Sum of all the probabilities of X for each x in $R_X$










    share|cite|improve this question









    $endgroup$















      0












      0








      0





      $begingroup$


      I’m reading through a chapter in https://www.probabilitycourse.com



      There is a section ...




      Consider a discrete random variable X with range $Range(X)=R_X$. Note that by definition the PMF is a probability measure, so it satisfies all properties of a probability measure. In particular, we have





      • $0 leq P_X(x) leq 1$ for all $x$, and

      • $Sigma_{ x in R_X}P_X(x)=1$




      How do I read the bottom equation? Is it ...



      Sum of all the probabilities of X for each x in $R_X$










      share|cite|improve this question









      $endgroup$




      I’m reading through a chapter in https://www.probabilitycourse.com



      There is a section ...




      Consider a discrete random variable X with range $Range(X)=R_X$. Note that by definition the PMF is a probability measure, so it satisfies all properties of a probability measure. In particular, we have





      • $0 leq P_X(x) leq 1$ for all $x$, and

      • $Sigma_{ x in R_X}P_X(x)=1$




      How do I read the bottom equation? Is it ...



      Sum of all the probabilities of X for each x in $R_X$







      probability random-variables






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      share|cite|improve this question











      share|cite|improve this question




      share|cite|improve this question










      asked Feb 1 at 22:05









      Chris SnowChris Snow

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      1738






















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          $begingroup$

          You sum over all x's which are in $R_x=Range(X)={x in mathbb{R} | x=X(omega) }$ for some $omega$ in the sample sample}.



          So yes, you read that correctly.






          share|cite|improve this answer









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            1 Answer
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            $begingroup$

            You sum over all x's which are in $R_x=Range(X)={x in mathbb{R} | x=X(omega) }$ for some $omega$ in the sample sample}.



            So yes, you read that correctly.






            share|cite|improve this answer









            $endgroup$


















              1












              $begingroup$

              You sum over all x's which are in $R_x=Range(X)={x in mathbb{R} | x=X(omega) }$ for some $omega$ in the sample sample}.



              So yes, you read that correctly.






              share|cite|improve this answer









              $endgroup$
















                1












                1








                1





                $begingroup$

                You sum over all x's which are in $R_x=Range(X)={x in mathbb{R} | x=X(omega) }$ for some $omega$ in the sample sample}.



                So yes, you read that correctly.






                share|cite|improve this answer









                $endgroup$



                You sum over all x's which are in $R_x=Range(X)={x in mathbb{R} | x=X(omega) }$ for some $omega$ in the sample sample}.



                So yes, you read that correctly.







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered Feb 1 at 22:11









                AlexandrosAlexandros

                1,0601413




                1,0601413






























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