variance of conditional multivariate gaussian












0












$begingroup$


I was playing around with Gaussian Distributions on my machine and I was interesting in making a pretty plot. I wanted to show the distribution of $x_1$ if $x_2$ was given if $x_1,x_2$ were distributed by a multivariate normal distribution.



enter image description here



The image feels "wrong". I would imagine that the red likelihood in the center of the blue distribution would be wider than at the edge. After double checking the code, I notice it might be the maths.



Maths on wikipedia as well as my books confirm that the distribution of $x_1$ conditional on $x_2$ = $a$ is multivariate normal $(x_1 | x_2 = a) sim N(hat{mu}, hat{Sigma})$ where



$$
bar{boldsymbolmu}
=
boldsymbolmu_1 + boldsymbolSigma_{12} boldsymbolSigma_{22}^{-1}
left(
mathbf{a} - boldsymbolmu_2
right)
$$



and covariance matrix



$$
overline{boldsymbolSigma}
=
boldsymbolSigma_{11} - boldsymbolSigma_{12} boldsymbolSigma_{22}^{-1} boldsymbolSigma_{21}.
$$



When looking at the maths, it seems that the variance of $p(x_1|x_2 = a)$ does not depend on the value of $a$. This is starting to feel very counter intuitive so I am wondering if I am missing something.



The code that generated the plot



import matplotlib.pylab as plt 



import torch

from torch.distributions import Normal as norm

from torch.distributions.multivariate_normal import MultivariateNormal as mvnorm



#@title different given values { run: "auto" }

g1 = -3.5 #@param {type:"slider", min:-4, max:4, step:0.1}

g2 = 0.2 #@param {type:"slider", min:-4, max:4, step:0.1}

g3 = 2.9 #@param {type:"slider", min:-4, max:4, step:0.1}



m = torch.tensor([0.0, 0.0])

c = torch.tensor([[1.0, 0.9], [0.9, 1.0]])



s = mvnorm(m, c).sample(sample_shape=(5000,))

s_np = s.numpy().reshape(5000, 2)

plt.figure(figsize=(6,5))

plt.scatter(s_np[:, 0], s_np[:, 1], alpha=0.3)



for g in [g1, g2, g3]:

    mu_pred = m[1] + c[0][1]/c[1][1]*(g - m[0])

    sigma_pred = c[1][1] - c[1][0]/c[0][0]*c[0][1]

    fitted_distr = norm(mu_pred, sigma_pred)

    print(f"g:{g:.3}, mu:{mu_pred:.2}, sigma:{sigma_pred:.4}")



    xs = torch.linspace(-4, 4, 300)

    likelihood = torch.exp(fitted_distr.log_prob(xs)).numpy()



    plt.plot(xs.numpy(), g + likelihood, c='red')





probability-distributions normal-distribution







share|cite|improve this question









$endgroup$

















    0












    $begingroup$


    I was playing around with Gaussian Distributions on my machine and I was interesting in making a pretty plot. I wanted to show the distribution of $x_1$ if $x_2$ was given if $x_1,x_2$ were distributed by a multivariate normal distribution.



    enter image description here



    The image feels "wrong". I would imagine that the red likelihood in the center of the blue distribution would be wider than at the edge. After double checking the code, I notice it might be the maths.



    Maths on wikipedia as well as my books confirm that the distribution of $x_1$ conditional on $x_2$ = $a$ is multivariate normal $(x_1 | x_2 = a) sim N(hat{mu}, hat{Sigma})$ where



    $$
    bar{boldsymbolmu}
    =
    boldsymbolmu_1 + boldsymbolSigma_{12} boldsymbolSigma_{22}^{-1}
    left(
    mathbf{a} - boldsymbolmu_2
    right)
    $$



    and covariance matrix



    $$
    overline{boldsymbolSigma}
    =
    boldsymbolSigma_{11} - boldsymbolSigma_{12} boldsymbolSigma_{22}^{-1} boldsymbolSigma_{21}.
    $$



    When looking at the maths, it seems that the variance of $p(x_1|x_2 = a)$ does not depend on the value of $a$. This is starting to feel very counter intuitive so I am wondering if I am missing something.



    The code that generated the plot



    import matplotlib.pylab as plt 
    

    
import torch
    
from torch.distributions import Normal as norm
    
from torch.distributions.multivariate_normal import MultivariateNormal as mvnorm
    

    
#@title different given values { run: "auto" }
    
g1 = -3.5 #@param {type:"slider", min:-4, max:4, step:0.1}
    
g2 = 0.2 #@param {type:"slider", min:-4, max:4, step:0.1}
    
g3 = 2.9 #@param {type:"slider", min:-4, max:4, step:0.1}
    

    
m = torch.tensor([0.0, 0.0])
    
c = torch.tensor([[1.0, 0.9], [0.9, 1.0]])
    

    
s = mvnorm(m, c).sample(sample_shape=(5000,))
    
s_np = s.numpy().reshape(5000, 2)
    
plt.figure(figsize=(6,5))
    
plt.scatter(s_np[:, 0], s_np[:, 1], alpha=0.3)
    

    
for g in [g1, g2, g3]:
    
    mu_pred = m[1] + c[0][1]/c[1][1]*(g - m[0])
    
    sigma_pred = c[1][1] - c[1][0]/c[0][0]*c[0][1]
    
    fitted_distr = norm(mu_pred, sigma_pred)
    
    print(f"g:{g:.3}, mu:{mu_pred:.2}, sigma:{sigma_pred:.4}")
    

    
    xs = torch.linspace(-4, 4, 300)
    
    likelihood = torch.exp(fitted_distr.log_prob(xs)).numpy()
    

    
    plt.plot(xs.numpy(), g + likelihood, c='red')





    probability-distributions normal-distribution







    share|cite|improve this question









    $endgroup$















      0












      0








      0





      $begingroup$


      I was playing around with Gaussian Distributions on my machine and I was interesting in making a pretty plot. I wanted to show the distribution of $x_1$ if $x_2$ was given if $x_1,x_2$ were distributed by a multivariate normal distribution.



      enter image description here



      The image feels "wrong". I would imagine that the red likelihood in the center of the blue distribution would be wider than at the edge. After double checking the code, I notice it might be the maths.



      Maths on wikipedia as well as my books confirm that the distribution of $x_1$ conditional on $x_2$ = $a$ is multivariate normal $(x_1 | x_2 = a) sim N(hat{mu}, hat{Sigma})$ where



      $$
      bar{boldsymbolmu}
      =
      boldsymbolmu_1 + boldsymbolSigma_{12} boldsymbolSigma_{22}^{-1}
      left(
      mathbf{a} - boldsymbolmu_2
      right)
      $$



      and covariance matrix



      $$
      overline{boldsymbolSigma}
      =
      boldsymbolSigma_{11} - boldsymbolSigma_{12} boldsymbolSigma_{22}^{-1} boldsymbolSigma_{21}.
      $$



      When looking at the maths, it seems that the variance of $p(x_1|x_2 = a)$ does not depend on the value of $a$. This is starting to feel very counter intuitive so I am wondering if I am missing something.



      The code that generated the plot



      import matplotlib.pylab as plt 
      

      
import torch
      
from torch.distributions import Normal as norm
      
from torch.distributions.multivariate_normal import MultivariateNormal as mvnorm
      

      
#@title different given values { run: "auto" }
      
g1 = -3.5 #@param {type:"slider", min:-4, max:4, step:0.1}
      
g2 = 0.2 #@param {type:"slider", min:-4, max:4, step:0.1}
      
g3 = 2.9 #@param {type:"slider", min:-4, max:4, step:0.1}
      

      
m = torch.tensor([0.0, 0.0])
      
c = torch.tensor([[1.0, 0.9], [0.9, 1.0]])
      

      
s = mvnorm(m, c).sample(sample_shape=(5000,))
      
s_np = s.numpy().reshape(5000, 2)
      
plt.figure(figsize=(6,5))
      
plt.scatter(s_np[:, 0], s_np[:, 1], alpha=0.3)
      

      
for g in [g1, g2, g3]:
      
    mu_pred = m[1] + c[0][1]/c[1][1]*(g - m[0])
      
    sigma_pred = c[1][1] - c[1][0]/c[0][0]*c[0][1]
      
    fitted_distr = norm(mu_pred, sigma_pred)
      
    print(f"g:{g:.3}, mu:{mu_pred:.2}, sigma:{sigma_pred:.4}")
      

      
    xs = torch.linspace(-4, 4, 300)
      
    likelihood = torch.exp(fitted_distr.log_prob(xs)).numpy()
      

      
    plt.plot(xs.numpy(), g + likelihood, c='red')





      probability-distributions normal-distribution







      share|cite|improve this question









      $endgroup$




      I was playing around with Gaussian Distributions on my machine and I was interesting in making a pretty plot. I wanted to show the distribution of $x_1$ if $x_2$ was given if $x_1,x_2$ were distributed by a multivariate normal distribution.



      enter image description here



      The image feels "wrong". I would imagine that the red likelihood in the center of the blue distribution would be wider than at the edge. After double checking the code, I notice it might be the maths.



      Maths on wikipedia as well as my books confirm that the distribution of $x_1$ conditional on $x_2$ = $a$ is multivariate normal $(x_1 | x_2 = a) sim N(hat{mu}, hat{Sigma})$ where



      $$
      bar{boldsymbolmu}
      =
      boldsymbolmu_1 + boldsymbolSigma_{12} boldsymbolSigma_{22}^{-1}
      left(
      mathbf{a} - boldsymbolmu_2
      right)
      $$



      and covariance matrix



      $$
      overline{boldsymbolSigma}
      =
      boldsymbolSigma_{11} - boldsymbolSigma_{12} boldsymbolSigma_{22}^{-1} boldsymbolSigma_{21}.
      $$



      When looking at the maths, it seems that the variance of $p(x_1|x_2 = a)$ does not depend on the value of $a$. This is starting to feel very counter intuitive so I am wondering if I am missing something.



      The code that generated the plot



      import matplotlib.pylab as plt 
      

      
import torch
      
from torch.distributions import Normal as norm
      
from torch.distributions.multivariate_normal import MultivariateNormal as mvnorm
      

      
#@title different given values { run: "auto" }
      
g1 = -3.5 #@param {type:"slider", min:-4, max:4, step:0.1}
      
g2 = 0.2 #@param {type:"slider", min:-4, max:4, step:0.1}
      
g3 = 2.9 #@param {type:"slider", min:-4, max:4, step:0.1}
      

      
m = torch.tensor([0.0, 0.0])
      
c = torch.tensor([[1.0, 0.9], [0.9, 1.0]])
      

      
s = mvnorm(m, c).sample(sample_shape=(5000,))
      
s_np = s.numpy().reshape(5000, 2)
      
plt.figure(figsize=(6,5))
      
plt.scatter(s_np[:, 0], s_np[:, 1], alpha=0.3)
      

      
for g in [g1, g2, g3]:
      
    mu_pred = m[1] + c[0][1]/c[1][1]*(g - m[0])
      
    sigma_pred = c[1][1] - c[1][0]/c[0][0]*c[0][1]
      
    fitted_distr = norm(mu_pred, sigma_pred)
      
    print(f"g:{g:.3}, mu:{mu_pred:.2}, sigma:{sigma_pred:.4}")
      

      
    xs = torch.linspace(-4, 4, 300)
      
    likelihood = torch.exp(fitted_distr.log_prob(xs)).numpy()
      

      
    plt.plot(xs.numpy(), g + likelihood, c='red')





      probability-distributions normal-distribution




      probability-distributions normal-distribution






      share|cite|improve this question













      share|cite|improve this question











      share|cite|improve this question




      share|cite|improve this question










      asked Jan 6 at 8:38









      Vincent WarmerdamVincent Warmerdam

      15217




      15217






















          1 Answer
          1






          active

          oldest

          votes


















          0












          $begingroup$

          This question was also asked on another stack-exchange website and that question has been answered there.






          share|cite|improve this answer









          $endgroup$













            Your Answer





            StackExchange.ifUsing("editor", function () {
            return StackExchange.using("mathjaxEditing", function () {
            StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
            StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
            });
            });
            }, "mathjax-editing");

            StackExchange.ready(function() {
            var channelOptions = {
            tags: "".split(" "),
            id: "69"
            };
            initTagRenderer("".split(" "), "".split(" "), channelOptions);

            StackExchange.using("externalEditor", function() {
            // Have to fire editor after snippets, if snippets enabled
            if (StackExchange.settings.snippets.snippetsEnabled) {
            StackExchange.using("snippets", function() {
            createEditor();
            });
            }
            else {
            createEditor();
            }
            });

            function createEditor() {
            StackExchange.prepareEditor({
            heartbeatType: 'answer',
            autoActivateHeartbeat: false,
            convertImagesToLinks: true,
            noModals: true,
            showLowRepImageUploadWarning: true,
            reputationToPostImages: 10,
            bindNavPrevention: true,
            postfix: "",
            imageUploader: {
            brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
            contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
            allowUrls: true
            },
            noCode: true, onDemand: true,
            discardSelector: ".discard-answer"
            ,immediatelyShowMarkdownHelp:true
            });


            }
            });














            draft saved

            draft discarded


















            StackExchange.ready(
            function () {
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f3063616%2fvariance-of-conditional-multivariate-gaussian%23new-answer', 'question_page');
            }
            );

            Post as a guest















            Required, but never shown

























            1 Answer
            1






            active

            oldest

            votes








            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            0












            $begingroup$

            This question was also asked on another stack-exchange website and that question has been answered there.






            share|cite|improve this answer









            $endgroup$


















              0












              $begingroup$

              This question was also asked on another stack-exchange website and that question has been answered there.






              share|cite|improve this answer









              $endgroup$
















                0












                0








                0





                $begingroup$

                This question was also asked on another stack-exchange website and that question has been answered there.






                share|cite|improve this answer









                $endgroup$



                This question was also asked on another stack-exchange website and that question has been answered there.







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered Jan 15 at 10:21









                Vincent WarmerdamVincent Warmerdam

                15217




                15217






























                    draft saved

                    draft discarded




















































                    Thanks for contributing an answer to Mathematics Stack Exchange!


                    • Please be sure to answer the question. Provide details and share your research!

                    But avoid …



                    • Asking for help, clarification, or responding to other answers.

                    • Making statements based on opinion; back them up with references or personal experience.


                    Use MathJax to format equations. MathJax reference.


                    To learn more, see our tips on writing great answers.




                    draft saved


                    draft discarded














                    StackExchange.ready(
                    function () {
                    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f3063616%2fvariance-of-conditional-multivariate-gaussian%23new-answer', 'question_page');
                    }
                    );

                    Post as a guest















                    Required, but never shown





















































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown

































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown







                    -

                    Popular posts from this blog

                    Ellipse (mathématiques)

                    Image
                    Pour les articles homonymes, voir Ellipse. L'ellipse est le lieu des points dont la somme des distances à deux points fixes, dits foyers, est constante. Section du cône ou projection du cercle. En géométrie, une ellipse est une courbe plane fermée obtenue par l’intersection d’un cône de révolution avec un plan, à condition que celui-ci coupe l'axe de rotation du cône ou du cylindre : c'est une conique d'excentricité strictement comprise entre 0 et 1. On peut également la définir comme le lieu des points dont la somme des distances à deux points fixes, dits foyers, est constante (sa construction par la méthode du jardinier est très simple). Dans la vie courante, l’ellipse est la forme qu'on perçoit en regardant un cercle en perspective, ou la figure formée par l’ombre d'un disque sur une surface plane. On retrouve aussi, en première approximation [ 1 ] , des ellipses dans les trajectoires des corps célestes (planètes, comètes ou satellites arti...
                    Read more

                    Quarter-circle Tiles

                    Image
                    up vote 0 down vote favorite I have $99$ identical square tiles, each with a quarter-circle drawn on it like this: [asy] size(1.5cm); draw(Arc((2,0),1,90,180),red+1); draw((0,0)--(2,0)--(2,2)--(0,2)--(0,0)); [/asy] When I arrange the tiles in a $9times 11$ rectangular grid, each with a random orientation, what is the expected value of the number of full circles I form? I think this problem has to do with finding the chance any given 2x2 square has a circle, but I can't find it. expected-value share | cite | improve this question asked Nov 20 at 15:03 6minecraftninja 1 2 ...
                    Read more

                    Mont Emei

                    Image
                    Mont Emei.mw-parser-output .entete.map{background-image:url("//upload.wikimedia.org/wikipedia/commons/7/7a/Picto_infobox_map.png")} Vue du sommet Wanfo du mont Emei Géographie Altitude 3 099  m , Wanfo Coordonnées 29° 31′ 11″ nord, 103° 19′ 57″ est Administration Pays   Chine Province Sichuan Ville-préfecture Leshan Géolocalisation sur la carte : Sichuan Mont Emei Géolocalisation sur la carte : Chine Mont Emei modifier   Paysage panoramique du mont Emei, incluant le paysage panoramique du grand Bouddha de Leshan *   Patrimoine mondial de l'UNESCO Passerelle en bois sur le versant ouest Pays   Chine Subdivision Sichuan Type Mixte Critères (iv) (vi) (x) Superficie 15 400 ha Numéro d’identification 779 Zone géographique Asie et Pacifique  ** Année d’inscription 1996 (20 e session) * Descriptif officiel UNESCO ** Classification géog...
                    Read more