What is the expectation of a random process with unequal probabilities?

[dman12]

Hello,

So I was asked this question the other day and don't really know how to go about it:

"Define a random process by:

X_n+1 = X_n+1 with probability 1/2
X_n+1 = 1/X_n with probability 1/2

Given that X₀= 100, find the expectation of X₁₀ "

I've only ever really met random processes like this when you consider equal step random walks and can use characteristic functions. How would I go about solving this problem?

Any help would be very much appreciated!

 

[jk22]

Maybe we could compute the average of the next step instead of having a tree ? We could also see the average as a fix point of the average recurrence relation.

 

[jk22]

In other words we write $$x_{n+1}=1/2*(x_n+1+1/x_n) $$

Computing the fix point is not exact for n=10 this would need a computer program but this method gives average at infinity is $$\phi=\frac {1+\sqrt {5}}{2}\approx 1.62$$

Average at ten using a program gave me 1.59

 

[Ray Vickson]

In other words we write $$x_{n+1}=1/2*(x_n+1+1/x_n) $$

Computing the fix point is not exact for n=10 this would need a computer program but this method gives average at infinity is $$\phi=\frac {1+\sqrt {5}}{2}\approx 1.62$$

Average at ten using a program gave me 1.59

No, that first equation you wrote is false:
E \left( \frac{1}{X_n} \right) \neq \frac{1}{E X_n}
For instance,
X_1 = \begin{cases} 101 &amp; \Pr = 1/2 \\<br /> \displaystyle \frac{1}{100} &amp; \Pr = 1/2<br /> \end{cases}<br />
Thus,
E X_1 = \frac{1}{2} \left( 101 + \frac{1}{100} \right) = 50.50500000
so
\frac{1}{E X_1} \doteq 0.01980001980
but
E \left( \frac{1}{X_1} \right) = \frac{1}{2} \left( \frac{1}{101} + 100 \right) \doteq 50.00495050<br />

Just for interest's sake: what was the nature of the program you used?

When I do it in Maple 11 I get the following $E X_n$ values:
\begin{array}{rl}<br /> n &amp; EX_n \\<br /> 2 &amp; 50.75497525 \\<br /> 3 &amp; 38.62872549 \\<br /> 4 &amp; 32.59731395 \\<br /> 5 &amp; 26.56027159 \\<br /> 6 &amp; 22.03256118 \\<br /> 7 &amp; 18.26083028 \\<br /> 8 &amp; 15.24344673 \\<br /> 9 &amp; 12.79228905 \\<br /> 10 &amp; 10.81267707 \\<br /> \end{array}<br />

Basically, starting from a sequence $V_n$ of $X_n$ values (which may have repeated values, so each entry has probability $2^{-n}$), I form two new sequences $M_{n+1} = [V_n(1)+1, \ldots ,V_n(2^n)+1]$ and $N_{n+1} = [1/V_n(1) , \ldots, 1/V_n(2^n) ]$, then get a new sequence $V_{n+1}$ of $X_{n+1}$ values by concatenating $M_{n+1}$ and $N_{n+1}$, then sorting the entries from smallest to largest. The sequence $V_{n+1}$ has $2^{n+1}$ entries, but many entries are duplicated, so each individual entry has probability $2^{-n-1}$. Of course, the sorting step is unnecessary, but makes the results easier to read.

Maple did all the computations of the $V_n$ in exact rational arithmetic, so roundoff errors are not an issue. However, I let the $V_n(i)$ values be converted to floating-point numbers in the computation of $EX_n$ because the exact rational value of $EX_n$ becomes a ratio of two huge integers when $n$ gets close to 10. In fact, the exact value of $EX_{10}$ is
\begin{array}{lcl} E X_{10} &amp;= &amp; \displaystyle\frac{I_{10}}{J_{10}}, \; \text{where}\\<br /> I_{10} &amp;=&amp; 1469579070445432944325748152877976451732541041634060024\\ &amp;&amp;653347052338236193574669483177095563513720147511715879 \\<br /> J_{10} &amp;=&amp; 1359126015084446126651435240624997445643066808400449966906136188164565\\<br /> &amp;&amp;14601326679253516313161435366438748160<br /> \end{array}<br />

 

[jk22]

This recurrence relation is not the recurrence relation of the average but the average recurrence relation, it's not intended to be exact.

I wrote a small C program( with an error, see the next post)

#include<stdio.h>

double avg=0.0;

void tree(double val, int depth)
{
   if(depth<10)
   {
     val=val+1.0;
     tree(val, depth+1);

     val=1.0/val;
     tree(val, depth+1);
   }
   else
     avg+=val;
}

int main(void)
{  
   int val0=100.0;

   tree(val0,0);

   printf("Avg : %lf\n",avg/1024.0);
}

 

[jk22]

I discovered an error in the code, it gives avg near 10.8.

#include<stdio.h>

double avg=0.0;

void tree(double val, int depth)
{
   if(depth<10)
   {
     tree(val+1.0, depth+1);
     tree(1.0/val, depth+1);
   }
   else
     avg+=val;
}

int main(void)
{  
   int val0=100.0;

   tree(val0,0);

   printf("Avg : %lf\n",avg/1024.0);
}

------------------
It's by making mistakes that I learn.

 

[jk22]

We obtain different results, i surely made a mistake. It builds a tree starting from 100 and using both recurrence relation up to the level then the final result is averaged.

 

[jk22]

I obtain different values, like rounded :

E7 18...
E8 15,...
E9 12,...

 

[Ray Vickson]

We obtain different results, i surely made a mistake. It builds a tree starting from 100 and using both recurrence relation up to the level then the final result is averaged.

Actually, we obtain the same results, or close to it. I had made an error in the recursions for $n = 7$ to $n = 10$, so those results for $EX_n$ were erroneous. They have now been corrected in the post, and I get $E X_{10} \approx 10.81$, as do you.

 

[jk22]

The question is now if we got to infinity does it converge to phi ?

 

[haruspex]

The question is now if we got to infinity does it converge to phi ?

I can show that asymptotically the mean value lies between 2.3 and 3.

 

[jk22]

I'm interested in the proof you have.

 

[haruspex]

I'm interested in the proof you have.

Whenever the sequence goes <1, the next step is >1. So I considered a subsequence which skips the <1 step. Sometimes that means we get two consecutive terms the same since it might follow the invert rule twice, so I skip the repeats too. This gets me to a sequence
$y_{n+1}=y_n+1$ with probability 2/3;
$=1/y_n+1$ with prob 1/3.
Let the mean of this sequence be $\mu$ and that of its inverse be $\nu$. We obtain $\mu=3+\nu$.
Since each term >1, 1>$\nu$>=$1/\mu$. Hence $4>\mu>3.3$.
Relating this back to the original sequence, the mean of that is $\frac 23\mu+\frac 13\nu$, and hence equals $\mu-1$.

 

[jk22]

I don't understand : how do you get $$\mu=3+\nu $$ ?

 

[haruspex]

I don't understand : how do you get $$\mu=3+\nu $$ ?

$\mu=E(y_{n+1})=\frac 23 E(y_n+1)+\frac 13 E(1/y_n+1)=\frac 23(\mu+1)+\frac 13(\nu+1)$ etc.

 

[jk22]

It is indeed a deep result.

 

[haruspex]

It is indeed a deep result.

Wasn't happy with the upper bound, so I tried comparing the y sequence with the sequence
$z_{n+1}=z_n+1$ with prob 2/3; =1 with prob 1/3.
In a sense which I believe can be made rigorous, this is everywhere less than the y sequence. At least, in the sense that E(z)<E(y) and, more importantly, E(1/z)>E(1/y). E(1/z)=ln(3)/2<0.55, so the average of the original sequence lies between 2.3 and 2.55.

Update: I generated some 2000-long y sequences starting at 3. The mean of a sequnce rarely fell outside the range 3.3 to 3.55.