Notation: What does the tilde below of the expectation mean?What is the meaning of superscript in $p_theta(x)$ and $mathbb E_thetaleft[S(theta)right]$?What does the notation $(textbfX perp textbfY , textbfWmid textbfZ)$ mean?Bounding the expectation of the difference between empirical vs generalization errorNotation is confusing me (sufficiency principle)Is value of ELBO a scalar or a distribution?Concentration of maximum of subexponential random variablesExpected value of Log Gamma function transformed Poisson random variableNotation and Transformation Rules of the Normal DistributionExpectation of Sufficient StatisticExpected value notation in GAN loss

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Notation: What does the tilde below of the expectation mean?


What is the meaning of superscript in $p_theta(x)$ and $mathbb E_thetaleft[S(theta)right]$?What does the notation $(textbfX perp textbfY , textbfWmid textbfZ)$ mean?Bounding the expectation of the difference between empirical vs generalization errorNotation is confusing me (sufficiency principle)Is value of ELBO a scalar or a distribution?Concentration of maximum of subexponential random variablesExpected value of Log Gamma function transformed Poisson random variableNotation and Transformation Rules of the Normal DistributionExpectation of Sufficient StatisticExpected value notation in GAN loss






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;








6












$begingroup$


I am reading about variational auto encoders, and there is the below loss function:



$l_i(Theta,phi) = - mathbbE_zsim q [log p_phi(x_i|z)] + KL(q_phi(z_i|x)||p(z))$



What does the notation $zsim q$ under $mathbbE$ mean? I just have
seen notations like $E(X)$ or $ langle Xrangle $ for expected value, $mathbbE$.



What does this notation generally mean when using $mathbbE_xsim y$ for some $x$ and some $y$?










share|cite











$endgroup$


















    6












    $begingroup$


    I am reading about variational auto encoders, and there is the below loss function:



    $l_i(Theta,phi) = - mathbbE_zsim q [log p_phi(x_i|z)] + KL(q_phi(z_i|x)||p(z))$



    What does the notation $zsim q$ under $mathbbE$ mean? I just have
    seen notations like $E(X)$ or $ langle Xrangle $ for expected value, $mathbbE$.



    What does this notation generally mean when using $mathbbE_xsim y$ for some $x$ and some $y$?










    share|cite











    $endgroup$














      6












      6








      6





      $begingroup$


      I am reading about variational auto encoders, and there is the below loss function:



      $l_i(Theta,phi) = - mathbbE_zsim q [log p_phi(x_i|z)] + KL(q_phi(z_i|x)||p(z))$



      What does the notation $zsim q$ under $mathbbE$ mean? I just have
      seen notations like $E(X)$ or $ langle Xrangle $ for expected value, $mathbbE$.



      What does this notation generally mean when using $mathbbE_xsim y$ for some $x$ and some $y$?










      share|cite











      $endgroup$




      I am reading about variational auto encoders, and there is the below loss function:



      $l_i(Theta,phi) = - mathbbE_zsim q [log p_phi(x_i|z)] + KL(q_phi(z_i|x)||p(z))$



      What does the notation $zsim q$ under $mathbbE$ mean? I just have
      seen notations like $E(X)$ or $ langle Xrangle $ for expected value, $mathbbE$.



      What does this notation generally mean when using $mathbbE_xsim y$ for some $x$ and some $y$?







      expected-value notation






      share|cite















      share|cite













      share|cite




      share|cite








      edited 23 mins ago









      Peter Mortensen

      20728




      20728










      asked 10 hours ago









      Code PopeCode Pope

      1336




      1336




















          1 Answer
          1






          active

          oldest

          votes


















          6












          $begingroup$

          $zsim q$ means that RV $Z$ is distributed with respect to $q$ function, i.e. $q(z)$, where $q(z)$ is a valid PDF/PMF. So, the expectation can be unfold as (assuming $z$ being continuous)
          $$E_zsim q[log_phi(x_i|z)]=int_-infty^infty log_phi (x_i|z) q(z) dz$$






          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            Ok. Thank you. I think now it is clear to me.
            $endgroup$
            – Code Pope
            9 hours ago











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          active

          oldest

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          6












          $begingroup$

          $zsim q$ means that RV $Z$ is distributed with respect to $q$ function, i.e. $q(z)$, where $q(z)$ is a valid PDF/PMF. So, the expectation can be unfold as (assuming $z$ being continuous)
          $$E_zsim q[log_phi(x_i|z)]=int_-infty^infty log_phi (x_i|z) q(z) dz$$






          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            Ok. Thank you. I think now it is clear to me.
            $endgroup$
            – Code Pope
            9 hours ago















          6












          $begingroup$

          $zsim q$ means that RV $Z$ is distributed with respect to $q$ function, i.e. $q(z)$, where $q(z)$ is a valid PDF/PMF. So, the expectation can be unfold as (assuming $z$ being continuous)
          $$E_zsim q[log_phi(x_i|z)]=int_-infty^infty log_phi (x_i|z) q(z) dz$$






          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            Ok. Thank you. I think now it is clear to me.
            $endgroup$
            – Code Pope
            9 hours ago













          6












          6








          6





          $begingroup$

          $zsim q$ means that RV $Z$ is distributed with respect to $q$ function, i.e. $q(z)$, where $q(z)$ is a valid PDF/PMF. So, the expectation can be unfold as (assuming $z$ being continuous)
          $$E_zsim q[log_phi(x_i|z)]=int_-infty^infty log_phi (x_i|z) q(z) dz$$






          share|cite|improve this answer











          $endgroup$



          $zsim q$ means that RV $Z$ is distributed with respect to $q$ function, i.e. $q(z)$, where $q(z)$ is a valid PDF/PMF. So, the expectation can be unfold as (assuming $z$ being continuous)
          $$E_zsim q[log_phi(x_i|z)]=int_-infty^infty log_phi (x_i|z) q(z) dz$$







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited 10 hours ago

























          answered 10 hours ago









          gunesgunes

          8,7091418




          8,7091418











          • $begingroup$
            Ok. Thank you. I think now it is clear to me.
            $endgroup$
            – Code Pope
            9 hours ago
















          • $begingroup$
            Ok. Thank you. I think now it is clear to me.
            $endgroup$
            – Code Pope
            9 hours ago















          $begingroup$
          Ok. Thank you. I think now it is clear to me.
          $endgroup$
          – Code Pope
          9 hours ago




          $begingroup$
          Ok. Thank you. I think now it is clear to me.
          $endgroup$
          – Code Pope
          9 hours ago

















          draft saved

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