Variance of Monte Carlo integration with importance sampling





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I am following these lecture slides on Monte Carlo integration with importance sampling. I am just implementing a very simple example: $int_{0}^{1} e^{x}dx$. For the importance sampling version, I rewrite $int_{0}^{1} e^{x}dx = int_{0}^{1} e^{x}/p(x)cdot p(x)dx$ where $p(x) = 2.5x^{1.5}$. Then



$$hat{I} = frac{1}{N}sum_{j=1}^{N} frac{f(x_{j})}{p(x_{j})},$$



where $x_{j}$ are sampled from $p(x_{j})$ (I use an inverse transform method here). For the variance, I have $sigma_{I}^{2} = hat{sigma}_{I}^{2}/N$ and



$$hat{sigma}_{I}^{2} = frac{1}{N} sum_{j=1}^{N} frac{f(x_{j})^{2}}{g(x_{j})^{2}} - hat{I}^{2}.$$



I know I should expected the variance to decrease with importance sampling, but a plot of the variance with $N$ shows that not much happens. Can anyone explain to me what I'm doing incorrectly? I'm not sure how the they are able to achieve such a drastic decrease in variance in the lecture slides.



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    $begingroup$


    I am following these lecture slides on Monte Carlo integration with importance sampling. I am just implementing a very simple example: $int_{0}^{1} e^{x}dx$. For the importance sampling version, I rewrite $int_{0}^{1} e^{x}dx = int_{0}^{1} e^{x}/p(x)cdot p(x)dx$ where $p(x) = 2.5x^{1.5}$. Then



    $$hat{I} = frac{1}{N}sum_{j=1}^{N} frac{f(x_{j})}{p(x_{j})},$$



    where $x_{j}$ are sampled from $p(x_{j})$ (I use an inverse transform method here). For the variance, I have $sigma_{I}^{2} = hat{sigma}_{I}^{2}/N$ and



    $$hat{sigma}_{I}^{2} = frac{1}{N} sum_{j=1}^{N} frac{f(x_{j})^{2}}{g(x_{j})^{2}} - hat{I}^{2}.$$



    I know I should expected the variance to decrease with importance sampling, but a plot of the variance with $N$ shows that not much happens. Can anyone explain to me what I'm doing incorrectly? I'm not sure how the they are able to achieve such a drastic decrease in variance in the lecture slides.



    enter image description here






    monte-carlo integral importance-sampling







    share|cite|improve this question









    $endgroup$















      3












      3








      3


      3



      $begingroup$


      I am following these lecture slides on Monte Carlo integration with importance sampling. I am just implementing a very simple example: $int_{0}^{1} e^{x}dx$. For the importance sampling version, I rewrite $int_{0}^{1} e^{x}dx = int_{0}^{1} e^{x}/p(x)cdot p(x)dx$ where $p(x) = 2.5x^{1.5}$. Then



      $$hat{I} = frac{1}{N}sum_{j=1}^{N} frac{f(x_{j})}{p(x_{j})},$$



      where $x_{j}$ are sampled from $p(x_{j})$ (I use an inverse transform method here). For the variance, I have $sigma_{I}^{2} = hat{sigma}_{I}^{2}/N$ and



      $$hat{sigma}_{I}^{2} = frac{1}{N} sum_{j=1}^{N} frac{f(x_{j})^{2}}{g(x_{j})^{2}} - hat{I}^{2}.$$



      I know I should expected the variance to decrease with importance sampling, but a plot of the variance with $N$ shows that not much happens. Can anyone explain to me what I'm doing incorrectly? I'm not sure how the they are able to achieve such a drastic decrease in variance in the lecture slides.



      enter image description here






      monte-carlo integral importance-sampling







      share|cite|improve this question









      $endgroup$




      I am following these lecture slides on Monte Carlo integration with importance sampling. I am just implementing a very simple example: $int_{0}^{1} e^{x}dx$. For the importance sampling version, I rewrite $int_{0}^{1} e^{x}dx = int_{0}^{1} e^{x}/p(x)cdot p(x)dx$ where $p(x) = 2.5x^{1.5}$. Then



      $$hat{I} = frac{1}{N}sum_{j=1}^{N} frac{f(x_{j})}{p(x_{j})},$$



      where $x_{j}$ are sampled from $p(x_{j})$ (I use an inverse transform method here). For the variance, I have $sigma_{I}^{2} = hat{sigma}_{I}^{2}/N$ and



      $$hat{sigma}_{I}^{2} = frac{1}{N} sum_{j=1}^{N} frac{f(x_{j})^{2}}{g(x_{j})^{2}} - hat{I}^{2}.$$



      I know I should expected the variance to decrease with importance sampling, but a plot of the variance with $N$ shows that not much happens. Can anyone explain to me what I'm doing incorrectly? I'm not sure how the they are able to achieve such a drastic decrease in variance in the lecture slides.



      enter image description here






      monte-carlo integral importance-sampling




      monte-carlo integral importance-sampling






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      asked Apr 1 at 18:26









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          $begingroup$

          enter image description hereThis is a good illustration of the dangers of importance sampling: while
          $$int_0^1 frac{e^x}{p(x)}, p(x)text{d} x = int_0^1 e^x text{d} x = I$$
          shows that $hat{I}_N$ is an unbiased estimator of $I$, this estimator does not have a finite variance since
          $$int_0^1 left(frac{e^x}{p(x)}right)^2, p(x)text{d} x = int_0^1 frac{e^{2x}}{2.5 x^{1.5}} text{d} x = infty$$
          since the integral diverges in $x=0$. For instance,



          > x=runif(1e7)^{1/2.5}
          
> range(exp(x)/x^{1.5})
          
[1]     2.718282 83403.685972


          shows that the weights can widely differ. I am not surprised at the figures reported in the above slides since



           > mean(exp(x)/x^{1.5})/2.5
          
 [1] 1.717576
          
 > var(exp(x)/x^{1.5})/(2.5)^2/1e7
          
 [1] 2.070953e-06


          but the empirical variance is rarely able to spot infinite variance importance sampling. (The graph shows that both the standard Monte Carlo estimate and the importance sampling version see the empirical standard deviation is decreasing as $N^{-1/2}$.)






          share|cite|improve this answer











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            $begingroup$

            enter image description hereThis is a good illustration of the dangers of importance sampling: while
            $$int_0^1 frac{e^x}{p(x)}, p(x)text{d} x = int_0^1 e^x text{d} x = I$$
            shows that $hat{I}_N$ is an unbiased estimator of $I$, this estimator does not have a finite variance since
            $$int_0^1 left(frac{e^x}{p(x)}right)^2, p(x)text{d} x = int_0^1 frac{e^{2x}}{2.5 x^{1.5}} text{d} x = infty$$
            since the integral diverges in $x=0$. For instance,



            > x=runif(1e7)^{1/2.5}
            
> range(exp(x)/x^{1.5})
            
[1]     2.718282 83403.685972


            shows that the weights can widely differ. I am not surprised at the figures reported in the above slides since



             > mean(exp(x)/x^{1.5})/2.5
            
 [1] 1.717576
            
 > var(exp(x)/x^{1.5})/(2.5)^2/1e7
            
 [1] 2.070953e-06


            but the empirical variance is rarely able to spot infinite variance importance sampling. (The graph shows that both the standard Monte Carlo estimate and the importance sampling version see the empirical standard deviation is decreasing as $N^{-1/2}$.)






            share|cite|improve this answer











            $endgroup$


















              3












              $begingroup$

              enter image description hereThis is a good illustration of the dangers of importance sampling: while
              $$int_0^1 frac{e^x}{p(x)}, p(x)text{d} x = int_0^1 e^x text{d} x = I$$
              shows that $hat{I}_N$ is an unbiased estimator of $I$, this estimator does not have a finite variance since
              $$int_0^1 left(frac{e^x}{p(x)}right)^2, p(x)text{d} x = int_0^1 frac{e^{2x}}{2.5 x^{1.5}} text{d} x = infty$$
              since the integral diverges in $x=0$. For instance,



              > x=runif(1e7)^{1/2.5}
              
> range(exp(x)/x^{1.5})
              
[1]     2.718282 83403.685972


              shows that the weights can widely differ. I am not surprised at the figures reported in the above slides since



               > mean(exp(x)/x^{1.5})/2.5
              
 [1] 1.717576
              
 > var(exp(x)/x^{1.5})/(2.5)^2/1e7
              
 [1] 2.070953e-06


              but the empirical variance is rarely able to spot infinite variance importance sampling. (The graph shows that both the standard Monte Carlo estimate and the importance sampling version see the empirical standard deviation is decreasing as $N^{-1/2}$.)






              share|cite|improve this answer











              $endgroup$
















                3












                3








                3





                $begingroup$

                enter image description hereThis is a good illustration of the dangers of importance sampling: while
                $$int_0^1 frac{e^x}{p(x)}, p(x)text{d} x = int_0^1 e^x text{d} x = I$$
                shows that $hat{I}_N$ is an unbiased estimator of $I$, this estimator does not have a finite variance since
                $$int_0^1 left(frac{e^x}{p(x)}right)^2, p(x)text{d} x = int_0^1 frac{e^{2x}}{2.5 x^{1.5}} text{d} x = infty$$
                since the integral diverges in $x=0$. For instance,



                > x=runif(1e7)^{1/2.5}
                
> range(exp(x)/x^{1.5})
                
[1]     2.718282 83403.685972


                shows that the weights can widely differ. I am not surprised at the figures reported in the above slides since



                 > mean(exp(x)/x^{1.5})/2.5
                
 [1] 1.717576
                
 > var(exp(x)/x^{1.5})/(2.5)^2/1e7
                
 [1] 2.070953e-06


                but the empirical variance is rarely able to spot infinite variance importance sampling. (The graph shows that both the standard Monte Carlo estimate and the importance sampling version see the empirical standard deviation is decreasing as $N^{-1/2}$.)






                share|cite|improve this answer











                $endgroup$



                enter image description hereThis is a good illustration of the dangers of importance sampling: while
                $$int_0^1 frac{e^x}{p(x)}, p(x)text{d} x = int_0^1 e^x text{d} x = I$$
                shows that $hat{I}_N$ is an unbiased estimator of $I$, this estimator does not have a finite variance since
                $$int_0^1 left(frac{e^x}{p(x)}right)^2, p(x)text{d} x = int_0^1 frac{e^{2x}}{2.5 x^{1.5}} text{d} x = infty$$
                since the integral diverges in $x=0$. For instance,



                > x=runif(1e7)^{1/2.5}
                
> range(exp(x)/x^{1.5})
                
[1]     2.718282 83403.685972


                shows that the weights can widely differ. I am not surprised at the figures reported in the above slides since



                 > mean(exp(x)/x^{1.5})/2.5
                
 [1] 1.717576
                
 > var(exp(x)/x^{1.5})/(2.5)^2/1e7
                
 [1] 2.070953e-06


                but the empirical variance is rarely able to spot infinite variance importance sampling. (The graph shows that both the standard Monte Carlo estimate and the importance sampling version see the empirical standard deviation is decreasing as $N^{-1/2}$.)







                share|cite|improve this answer














                share|cite|improve this answer



                share|cite|improve this answer








                edited Apr 1 at 19:02

























                answered Apr 1 at 18:44









                Xi'anXi'an

                59.2k897366




                59.2k897366






























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