GS 2003/Math - Collins
Lab 7
Markov Processes and Transition Matrices

In this lab you will create some transition matrices and use
MATLAB to study how the probability distribution changes over time.

Markov Process 

   A Markov process depends on two things: the transition matrix T
   and the initial probability distribution X(1) = X
   In MATLAB you write matrices starting with [ and ending with ],
   using commas (or spaces) to separate elements and semi-colons
   or returns to separate rows.

   For example:
      T = [0.5 0.3; 0.5 0.7];
      X = [1;0];

   You can check that T is a stochastic matrix (columns
   sum to 1), by typing sum(T) and seeing
   if all the values are 1 (or 1.0000).

   Once we have the transition matrix T and starting vector X
   we can compute the probability distributions at future times by
      for i = 1:99
         X(:,i+1) = T*X(:,i);

   After this code runs, X will be a 2x100 matrix with the
   values in column k representing the probability distributions
   after k-1 transitions.  You can see a plot of the results
   by typing plot(X').  You can add a legend
   with the legend command.

   Expected Values

   For some processes you can assign a value to each state and you
   want to know what the average value is at each transition, the
   so called expected value.  To compute it in MATLAB, you just set
   up a row vector val which has the values of each state
   and then compute exval = val*X;  Then
   you can plot the expected values by plot(exval)


   1. For each of these transition matrices, determine what the steady state
      distribution is (i.e. the distribution as the number of transitions
      goes to infinity).  Also, compute the probability of being in state 2
      after 3 transitions if you started in state 1.

      a.  T = [0.5 0.3; 0.5 0.7];
      b.  T = [0.8 0 0.2; 0.1 0.6 0.1; 0.1 0.4 0.7];

   2. A fox hunts in 3 territories: A, B, and C.  He never hunts in the same
      territory two days in a row.  If he hunts in A, he hunts in C the next
      day.  If he hunts in B or C, he is twice as likely to hunt in A than
      in the other territory.

      a. Over the long run, what proportion of his time does he spend in
         each territory?
      b. If he hunts in A on Sunday, what is the probability he will hunt
         in A on Wednesday? 

   3. You and a friend play a dice game where you both roll one die and
      if the number showing on your die is greater than or equal to the
      value on your friends die, then your friend pays you $1.   If your 
      die is less, then you pay your friend $1.  You stop the game when
      you are broke or have $4. 

      a. If you start the game with $1, what is the probability that you
         will quit with $4?  What about if you start with $2?
      b. If you start with $1, what is the average amount of money you'll
         have after 1, 2 or 3 games?

   4. (Challenges) 

      a. Brownian Motion:  Consider a particle in the middle of a NxN grid.
         At each transition, it has equal probabilities of moving one
         unit in each direction (NESW).  Assume when it hits the boundary
         it stops. Take N=7 to start, increase as desired.

         i. What is the probability that the particle is still moving after
            100 transitions? 200? 500? 1000?
        ii. What is the expected number of transitions before it stops?

      b. RISK: In a typical battle in the game of RISK, the attacker
         rolls 3 dice and the defender rolls 2 dice.  The top 2 dice
         of the attacker are compared one-to-one with the top dice
         of the defender.  When the attackers die is greater, the
         defender loses an army, and when the attackers die is
         less than or equal to the defender's, the attacker loses an
         army.  There is always a change of 2 armies with each battle,
         i.e either A loses 2, A and D each lose 1, or D loses 2.
         The probabilities for these are: 799/2731, 1420/4229, 792/2131

            Determine the probability that the defender army will
            be reduced to 1 or less before the attacker army is reduced
            to 3 or less, if the attacker starts with 20 armies and the
            defender starts with 5, 10, or 15 armies.
Last Modified: November 11, 1923