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Governor

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Fibonacci sequence: x(n 1) = x(n) x(n-1) Take x(0) = 1, x(1) = 1, then we get ... For any sequence generated by the Fibonacci rule: x(n 1)/x(n) - g ... – PowerPoint PPT presentation

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Title: Governor


1
Governors School for the Sciences
  • Mathematics

Day 7
2
MOTD Andrei A. Markov
  • 1856 to 1922
  • Stochastic processes
  • Markov Chains

3
Markov Process
  • State Diagram
  • Represent by System of Linear Difference
    Equations
  • Determine changes in the probabilities for being
    in a certain state

4
Key Transition Matrix
  • N states gt NxN Transition matrix T
  • T(i,j) probability of changing from
    state j to state i
  • X(n) vector of probabilities (at time n)
  • X(n1) TX(n) (Markov Process)
  • Properties 1. T never changes 2. At each
    time, all the probabilities in X(n) sum to
    1 3. Columns of T sum to 1

5
Boardwork
  • Keywords States, Distribution, Transition Matrix
  • Disease model (well, sick, dead)
  • Dice game (roll 1-5 in order before getting a 6)
  • Best of 3 Series

6
Disease Model
7
Dice Game
8
Dice Game Average Position
9
Best of 3 Series
  • Equal prob of winning ¼ probability of
    ending at (0,2), (1,2), (2,1), (2,0)
  • Team A has prob ¾ of winning game 9/16 prob
    of (2,0), 9/32 (2,1) 1/16 prob of (0,2), 3/32
    (1,2)

10
Exercise
  • Two states Not dormed Dormed
  • Transition Probabilities (per day) ND -gt ND
    0.8 ND -gt D 0.2 D -gt ND
    0.9 D -gt D 0.1
  • Construct the transition matrix
  • Determine if you start as Not dormed what is the
    prob of you being dormed after 1 day, 2 days, 3
    days

11
Results
  • A state is an absorbing state if once you get
    there you cant leave
  • If a system has only one absorbing state, then
    the distribution will tend towards that state
  • If it has 2 absorbing states, then the
    distribution will tend towards some split of
    those two states (depending on the initial
    distribution)
  • If it has no absorbing states then the
    distribution will tend towards some stable
    distribution

12
Break Time
13
Higher order DEs
  • 2nd order x(n1) f(x(n), x(n-1))
  • Fibonacci sequence x(n1) x(n) x(n-1)
  • Take x(0) 1, x(1) 1, then we get 2, 3, 5,
    8, 13, 21, 34, 55, 89,
  • Take x(0) 2, x(1) 4, then we get 6, 10,
    16, 26, 42, 68, 110,

14
Interesting facts
  • For any sequence generated by the Fibonacci rule
    x(n1)/x(n) -gt g (g (1sqrt(5))/2 the
    Golden ratio)
  • Many things in nature seem to follow the
    Fibonacci rule seeds in a sunflower, leaves on
    a pine cone (and other plants), etc.

15
Solving Linear DEs
  • Solve x(n1) ax(n)
  • Solve x(n1) ax(n) bx(n-1)
  • Solve x(n1) x(n) x(n-1)
  • Solve x(n1) ax(n) b
  • Solve x(n1) ax(n) bx(n-1) c

16
System of Difference Equations
  • Two sequences x(n) and y(n)
  • Difference equation relating both x(n1)
    f(x(n), y(n)) y(n1) g(x(n), y(n))
  • Example Predator-Prey x(n1) 1.1x(n)
    0.2y(n) y(n1) 0.2x(n) 0.95y(n) x
    rabbits, y - foxes

17
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18
Lab
  • Building transition matrices for a Markov process
    and studying the results
  • T 0.2, 0.3 0.8, 0.3 sum(T)
  • X 10
  • TX
  • X(,i1) TX(,i)
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