How to solve Markov transition rate matrix?












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I have some variables to find like x= [1x16 (x1,x2,x3,....x16 variables)] with condition that x1+x2+x3+....x16=1. I have also 16x16 matrix Q= [16x16 (real values)].



I need to solve the equation 'x*Q=x' as shown here. How can I solve it in Matlab or in any other language easily?










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    I have some variables to find like x= [1x16 (x1,x2,x3,....x16 variables)] with condition that x1+x2+x3+....x16=1. I have also 16x16 matrix Q= [16x16 (real values)].



    I need to solve the equation 'x*Q=x' as shown here. How can I solve it in Matlab or in any other language easily?










    share|improve this question



























      0












      0








      0








      I have some variables to find like x= [1x16 (x1,x2,x3,....x16 variables)] with condition that x1+x2+x3+....x16=1. I have also 16x16 matrix Q= [16x16 (real values)].



      I need to solve the equation 'x*Q=x' as shown here. How can I solve it in Matlab or in any other language easily?










      share|improve this question
















      I have some variables to find like x= [1x16 (x1,x2,x3,....x16 variables)] with condition that x1+x2+x3+....x16=1. I have also 16x16 matrix Q= [16x16 (real values)].



      I need to solve the equation 'x*Q=x' as shown here. How can I solve it in Matlab or in any other language easily?







      matlab matrix markov-chains markov-models






      share|improve this question















      share|improve this question













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      share|improve this question








      edited Nov 15 '18 at 6:40









      Billal Begueradj

      5,966132843




      5,966132843










      asked Nov 15 '18 at 5:36









      Abdullah1Abdullah1

      134




      134
























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          By transposition, your equation is equivalent to Q'y=1y where y:=x' (a column vector) where Q' is the transpose of Q (matlab notation...) which means that y is an eigenvector associated with eigenvalue 1 for matrix Q'. Such an eigenvector always exists for a Markov matrix. Let s be the sum of the entries of column vector y. Two cases can occur :




          • either s is not 0 ; then it suffices to divide all coordinates of y by s : we obtain a vector that is still an eigenvector, with a coordinate sum equal to 1.


          • or s=0 and there is no solution to your problem.



          Here is a Matlab program that does the work for a 3 x 3 matrix :



          M=[.2 .3 .5
          .1 .8 .1
          .4 .4 .2]
          [P,D]=eig(M')
          Y=P(:,3)
          M'*Y - Y,% should be 0
          Z=Y/sum(Y),%the sum of Z's coordinates is 1
          M'*Z-Z,% should be 0





          share|improve this answer

























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            By transposition, your equation is equivalent to Q'y=1y where y:=x' (a column vector) where Q' is the transpose of Q (matlab notation...) which means that y is an eigenvector associated with eigenvalue 1 for matrix Q'. Such an eigenvector always exists for a Markov matrix. Let s be the sum of the entries of column vector y. Two cases can occur :




            • either s is not 0 ; then it suffices to divide all coordinates of y by s : we obtain a vector that is still an eigenvector, with a coordinate sum equal to 1.


            • or s=0 and there is no solution to your problem.



            Here is a Matlab program that does the work for a 3 x 3 matrix :



            M=[.2 .3 .5
            .1 .8 .1
            .4 .4 .2]
            [P,D]=eig(M')
            Y=P(:,3)
            M'*Y - Y,% should be 0
            Z=Y/sum(Y),%the sum of Z's coordinates is 1
            M'*Z-Z,% should be 0





            share|improve this answer






























              0














              By transposition, your equation is equivalent to Q'y=1y where y:=x' (a column vector) where Q' is the transpose of Q (matlab notation...) which means that y is an eigenvector associated with eigenvalue 1 for matrix Q'. Such an eigenvector always exists for a Markov matrix. Let s be the sum of the entries of column vector y. Two cases can occur :




              • either s is not 0 ; then it suffices to divide all coordinates of y by s : we obtain a vector that is still an eigenvector, with a coordinate sum equal to 1.


              • or s=0 and there is no solution to your problem.



              Here is a Matlab program that does the work for a 3 x 3 matrix :



              M=[.2 .3 .5
              .1 .8 .1
              .4 .4 .2]
              [P,D]=eig(M')
              Y=P(:,3)
              M'*Y - Y,% should be 0
              Z=Y/sum(Y),%the sum of Z's coordinates is 1
              M'*Z-Z,% should be 0





              share|improve this answer




























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                0







                By transposition, your equation is equivalent to Q'y=1y where y:=x' (a column vector) where Q' is the transpose of Q (matlab notation...) which means that y is an eigenvector associated with eigenvalue 1 for matrix Q'. Such an eigenvector always exists for a Markov matrix. Let s be the sum of the entries of column vector y. Two cases can occur :




                • either s is not 0 ; then it suffices to divide all coordinates of y by s : we obtain a vector that is still an eigenvector, with a coordinate sum equal to 1.


                • or s=0 and there is no solution to your problem.



                Here is a Matlab program that does the work for a 3 x 3 matrix :



                M=[.2 .3 .5
                .1 .8 .1
                .4 .4 .2]
                [P,D]=eig(M')
                Y=P(:,3)
                M'*Y - Y,% should be 0
                Z=Y/sum(Y),%the sum of Z's coordinates is 1
                M'*Z-Z,% should be 0





                share|improve this answer















                By transposition, your equation is equivalent to Q'y=1y where y:=x' (a column vector) where Q' is the transpose of Q (matlab notation...) which means that y is an eigenvector associated with eigenvalue 1 for matrix Q'. Such an eigenvector always exists for a Markov matrix. Let s be the sum of the entries of column vector y. Two cases can occur :




                • either s is not 0 ; then it suffices to divide all coordinates of y by s : we obtain a vector that is still an eigenvector, with a coordinate sum equal to 1.


                • or s=0 and there is no solution to your problem.



                Here is a Matlab program that does the work for a 3 x 3 matrix :



                M=[.2 .3 .5
                .1 .8 .1
                .4 .4 .2]
                [P,D]=eig(M')
                Y=P(:,3)
                M'*Y - Y,% should be 0
                Z=Y/sum(Y),%the sum of Z's coordinates is 1
                M'*Z-Z,% should be 0






                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Nov 15 '18 at 17:15

























                answered Nov 15 '18 at 17:02









                Jean Marie BeckerJean Marie Becker

                1585




                1585
































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