Proof of the Markov Property when Taking “Two steps at a time”.











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I have seen the solution, but I would like to understand how it actually works.



A new variable is defined, $Z_n = (X_n, X_{n-1})$, this is kind of like the join probability of 2 consecutive steps; the smallest value of n in this case is 1. The proof proceeds as follows:




  1. $P(Z_n = z_n|Z_{n-1}, Z_{n-2}...Z_1 = z_1)$


  2. The above is equal to : $P(X_n = x_n, X_{n-1} = x_{n-1}|X_{n-1} = x_{n-1}, X_{n-2} = x_{n-2}...X_1 = x_1)$


  3. Which is equal to: $P(X_n = x_n|X_{n-1} = x_{n-1})$


  4. Which finally, is equal to : $P(Z_n=z_n|Z_{n-1} = z_{n-1})$



I'm struggling to understand how the RHS of the 2nd step came about, and also why the 3rd step automatically implies the last step is true.



Your help would be appreciated greatly, thanks in advance !










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    up vote
    1
    down vote

    favorite












    I have seen the solution, but I would like to understand how it actually works.



    A new variable is defined, $Z_n = (X_n, X_{n-1})$, this is kind of like the join probability of 2 consecutive steps; the smallest value of n in this case is 1. The proof proceeds as follows:




    1. $P(Z_n = z_n|Z_{n-1}, Z_{n-2}...Z_1 = z_1)$


    2. The above is equal to : $P(X_n = x_n, X_{n-1} = x_{n-1}|X_{n-1} = x_{n-1}, X_{n-2} = x_{n-2}...X_1 = x_1)$


    3. Which is equal to: $P(X_n = x_n|X_{n-1} = x_{n-1})$


    4. Which finally, is equal to : $P(Z_n=z_n|Z_{n-1} = z_{n-1})$



    I'm struggling to understand how the RHS of the 2nd step came about, and also why the 3rd step automatically implies the last step is true.



    Your help would be appreciated greatly, thanks in advance !










    share|cite|improve this question
























      up vote
      1
      down vote

      favorite









      up vote
      1
      down vote

      favorite











      I have seen the solution, but I would like to understand how it actually works.



      A new variable is defined, $Z_n = (X_n, X_{n-1})$, this is kind of like the join probability of 2 consecutive steps; the smallest value of n in this case is 1. The proof proceeds as follows:




      1. $P(Z_n = z_n|Z_{n-1}, Z_{n-2}...Z_1 = z_1)$


      2. The above is equal to : $P(X_n = x_n, X_{n-1} = x_{n-1}|X_{n-1} = x_{n-1}, X_{n-2} = x_{n-2}...X_1 = x_1)$


      3. Which is equal to: $P(X_n = x_n|X_{n-1} = x_{n-1})$


      4. Which finally, is equal to : $P(Z_n=z_n|Z_{n-1} = z_{n-1})$



      I'm struggling to understand how the RHS of the 2nd step came about, and also why the 3rd step automatically implies the last step is true.



      Your help would be appreciated greatly, thanks in advance !










      share|cite|improve this question













      I have seen the solution, but I would like to understand how it actually works.



      A new variable is defined, $Z_n = (X_n, X_{n-1})$, this is kind of like the join probability of 2 consecutive steps; the smallest value of n in this case is 1. The proof proceeds as follows:




      1. $P(Z_n = z_n|Z_{n-1}, Z_{n-2}...Z_1 = z_1)$


      2. The above is equal to : $P(X_n = x_n, X_{n-1} = x_{n-1}|X_{n-1} = x_{n-1}, X_{n-2} = x_{n-2}...X_1 = x_1)$


      3. Which is equal to: $P(X_n = x_n|X_{n-1} = x_{n-1})$


      4. Which finally, is equal to : $P(Z_n=z_n|Z_{n-1} = z_{n-1})$



      I'm struggling to understand how the RHS of the 2nd step came about, and also why the 3rd step automatically implies the last step is true.



      Your help would be appreciated greatly, thanks in advance !







      stochastic-processes markov-chains random-walk






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      asked Nov 20 at 22:25









      PEze

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          You can use the rule $P(AB|C) = P(A|BC)P(B|C)$ on step 2, where $A$ is the event $X_n=x_n$, $B$ is the event $X_{n-1}=x_{n-1}$, and $C$ is the event $X_{n-1}=x_{n-1}, ; ... ; X_1=x_1$. By the Markov property, you can see that $P(A|BC)$ will simply reduce to $P(A|B)$. Given the definition of $B$ and $C$, it is trivial to see why $P(B|C) = 1$. This will lead you from step 2 to step 3.



          Let's expand step 4:



          $P(Z_n=z_n|Z_{n-1}=z_{n-1})$



          $=P(X_n=x_n, X_{n-1}=x_{n-1}|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$



          $=P(X_n=x_n|X_{n-1}=x_{n-1}, X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (using $P(AB|C) = P(A|BC)P(B|C)$)



          $=P(X_n=x_n|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (removing redundant term)



          $=P(X_n=x_n|X_{n-1}=x_{n-1})$ (Markov property)






          share|cite|improve this answer





















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            You can use the rule $P(AB|C) = P(A|BC)P(B|C)$ on step 2, where $A$ is the event $X_n=x_n$, $B$ is the event $X_{n-1}=x_{n-1}$, and $C$ is the event $X_{n-1}=x_{n-1}, ; ... ; X_1=x_1$. By the Markov property, you can see that $P(A|BC)$ will simply reduce to $P(A|B)$. Given the definition of $B$ and $C$, it is trivial to see why $P(B|C) = 1$. This will lead you from step 2 to step 3.



            Let's expand step 4:



            $P(Z_n=z_n|Z_{n-1}=z_{n-1})$



            $=P(X_n=x_n, X_{n-1}=x_{n-1}|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$



            $=P(X_n=x_n|X_{n-1}=x_{n-1}, X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (using $P(AB|C) = P(A|BC)P(B|C)$)



            $=P(X_n=x_n|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (removing redundant term)



            $=P(X_n=x_n|X_{n-1}=x_{n-1})$ (Markov property)






            share|cite|improve this answer

























              up vote
              0
              down vote













              You can use the rule $P(AB|C) = P(A|BC)P(B|C)$ on step 2, where $A$ is the event $X_n=x_n$, $B$ is the event $X_{n-1}=x_{n-1}$, and $C$ is the event $X_{n-1}=x_{n-1}, ; ... ; X_1=x_1$. By the Markov property, you can see that $P(A|BC)$ will simply reduce to $P(A|B)$. Given the definition of $B$ and $C$, it is trivial to see why $P(B|C) = 1$. This will lead you from step 2 to step 3.



              Let's expand step 4:



              $P(Z_n=z_n|Z_{n-1}=z_{n-1})$



              $=P(X_n=x_n, X_{n-1}=x_{n-1}|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$



              $=P(X_n=x_n|X_{n-1}=x_{n-1}, X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (using $P(AB|C) = P(A|BC)P(B|C)$)



              $=P(X_n=x_n|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (removing redundant term)



              $=P(X_n=x_n|X_{n-1}=x_{n-1})$ (Markov property)






              share|cite|improve this answer























                up vote
                0
                down vote










                up vote
                0
                down vote









                You can use the rule $P(AB|C) = P(A|BC)P(B|C)$ on step 2, where $A$ is the event $X_n=x_n$, $B$ is the event $X_{n-1}=x_{n-1}$, and $C$ is the event $X_{n-1}=x_{n-1}, ; ... ; X_1=x_1$. By the Markov property, you can see that $P(A|BC)$ will simply reduce to $P(A|B)$. Given the definition of $B$ and $C$, it is trivial to see why $P(B|C) = 1$. This will lead you from step 2 to step 3.



                Let's expand step 4:



                $P(Z_n=z_n|Z_{n-1}=z_{n-1})$



                $=P(X_n=x_n, X_{n-1}=x_{n-1}|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$



                $=P(X_n=x_n|X_{n-1}=x_{n-1}, X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (using $P(AB|C) = P(A|BC)P(B|C)$)



                $=P(X_n=x_n|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (removing redundant term)



                $=P(X_n=x_n|X_{n-1}=x_{n-1})$ (Markov property)






                share|cite|improve this answer












                You can use the rule $P(AB|C) = P(A|BC)P(B|C)$ on step 2, where $A$ is the event $X_n=x_n$, $B$ is the event $X_{n-1}=x_{n-1}$, and $C$ is the event $X_{n-1}=x_{n-1}, ; ... ; X_1=x_1$. By the Markov property, you can see that $P(A|BC)$ will simply reduce to $P(A|B)$. Given the definition of $B$ and $C$, it is trivial to see why $P(B|C) = 1$. This will lead you from step 2 to step 3.



                Let's expand step 4:



                $P(Z_n=z_n|Z_{n-1}=z_{n-1})$



                $=P(X_n=x_n, X_{n-1}=x_{n-1}|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$



                $=P(X_n=x_n|X_{n-1}=x_{n-1}, X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (using $P(AB|C) = P(A|BC)P(B|C)$)



                $=P(X_n=x_n|X_{n-1}=x_{n-1},X_{n-2}=x_{n-2})$ (removing redundant term)



                $=P(X_n=x_n|X_{n-1}=x_{n-1})$ (Markov property)







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered Dec 3 at 3:00









                Aditya Dua

                6458




                6458






























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