278 Chapter Three. Maps Between Spaces[Feller], p. 4243 There has been much interest in whether industries in the United States aremoving from the Northeast and North Central regions to the South and West,motivated by the warmer climate, by lower wages, and by less unionization. Here isthe transition matrix for large firms in Electric and Electronic Equipment ([Kelton],p. 43)NENCSWZNE NC S W Z0.787 0 0 0.111 0.1020 0.966 0.034 0 00 0.063 0.937 0 00 0 0.074 0.612 0.3140.021 0.009 0.005 0.010 0.954For example, a firm in the Northeast region will be in the West region next yearwith probability 0.111. (The Z entry is a “birth-death” state. For instance, withprobability 0.102 a large Electric and Electronic Equipment firm from the Northeastwill move out of this system next year: go out of business, move abroad, ormove to another category of firm. There is a 0.021 probability that a firm in theNational Census of Manufacturers will move into Electronics, or be created, ormove in from abroad, into the Northeast. Finally, with probability 0.954 a firmout of the categories will stay out, according to this research.)(a) Does the Markov model assumption of lack of history seem justified?(b) Assume that the initial distribution is even, except that the value at Z is0.9. Compute the vectors for n = 1 through n = 4.(c) Suppose that the initial distribution is this.NE NC S W Z0.0000 0.6522 0.3478 0.0000 0.0000Calculate the distributions for n = 1 through n = 4.(d) Find the distribution for n = 50 and n = 51. Has the system settled downto an equilibrium?4 This model has been suggested for some kinds of learning ([Wickens], p. 41). Thelearner starts in an undecided state s U . Eventually the learner has to decide to doeither response A (that is, end in state s A ) or response B (ending in s B ). However,the learner doesn’t jump right from being undecided to being sure A is the correctthing to do (or B). Instead, the learner spends some time in a “tentative-A”state, or a “tentative-B” state, trying the response out (denoted here t A and t B ).Imagine that once the learner has decided, it is final, so once s A or s B is enteredit is never left. For the other state changes, imagine a transition is made withprobability p in either direction.(a) Construct the transition matrix.(b) Take p = 0.25 and take the initial vector to be 1 at s U . Run this for fivesteps. What is the chance of ending up at s A?(c) Do the same for p = 0.20.(d) Graph p versus the chance of ending at s A. Is there a threshold value for p,above which the learner is almost sure not to take longer than five steps?5 A certain town is in a certain country (this is a hypothetical problem). Each yearten percent of the town dwellers move to other parts of the country. Each yearone percent of the people from elsewhere move to the town. Assume that thereare two states s T , living in town, and s C, living elsewhere.(a) Construct the transistion matrix.
Topic: Markov Chains 279(b) Starting with an initial distribution s T = 0.3 and s C = 0.7, get the resultsfor the first ten years.(c) Do the same for s T = 0.2.(d) Are the two outcomes alike or different?6 For the World Series application, use a computer to generate the seven vectorsfor p = 0.55 and p = 0.6.(a) What is the chance of the National League team winning it all, even thoughthey have only a probability of 0.45 or 0.40 of winning any one game?(b) Graph the probability p against the chance that the American League teamwins it all. Is there a threshold value — a p above which the better team isessentially ensured of winning?(Some sample code is included below.)7 A Markov matrix has each entry positive and each column sums to 1.(a) Check that the three transistion matrices shown in this Topic meet these twoconditions. Must any transition matrix do so?(b) Observe that if A⃗v 0 = ⃗v 1 and A⃗v 1 = ⃗v 2 then A 2 is a transition matrix from⃗v 0 to ⃗v 2 . Show that a power of a Markov matrix is also a Markov matrix.(c) Generalize the prior item by proving that the product of two appropriatelysizedMarkov matrices is a Markov matrix.Computer CodeThis script markov.m for the computer algebra system Octave was used togenerate the table of World Series outcomes. (The sharp character # marks therest of a line as a comment.)# Octave script file to compute chance of World Series outcomes.function w = markov(p,v)q = 1-p;A=[0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 0-0p,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 1-0q,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 0-1_0,p,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 2-00,q,p,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 1-10,0,q,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 0-2__0,0,0,p,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 3-00,0,0,q,p,0, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 2-10,0,0,0,q,p, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 1-2_0,0,0,0,0,q, 0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 0-30,0,0,0,0,0, p,0,0,0,1,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 4-00,0,0,0,0,0, q,p,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 3-1__0,0,0,0,0,0, 0,q,p,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 2-20,0,0,0,0,0, 0,0,q,p,0,0, 0,0,0,0,0,0, 0,0,0,0,0,0; # 1-30,0,0,0,0,0, 0,0,0,q,0,0, 0,0,1,0,0,0, 0,0,0,0,0,0; # 0-4_0,0,0,0,0,0, 0,0,0,0,0,p, 0,0,0,1,0,0, 0,0,0,0,0,0; # 4-10,0,0,0,0,0, 0,0,0,0,0,q, p,0,0,0,0,0, 0,0,0,0,0,0; # 3-20,0,0,0,0,0, 0,0,0,0,0,0, q,p,0,0,0,0, 0,0,0,0,0,0; # 2-3__0,0,0,0,0,0, 0,0,0,0,0,0, 0,q,0,0,0,0, 1,0,0,0,0,0; # 1-40,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,p,0, 0,1,0,0,0,0; # 4-20,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,q,p, 0,0,0,0,0,0; # 3-3_0,0,0,0,0,0, 0,0,0,0,0,0, 0,0,0,0,0,q, 0,0,0,1,0,0; # 2-4
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Linear AlgebraJim Hefferon( 13)( 21
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PrefaceThis book helps students to
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Chapter Three: Maps Between Spaces
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Topic: Method of Powers 395Topic: M
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AppendixMathematics is made of argu
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A-3shows that Q holds whenever P do
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A-5more factors than any other’ w
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A-7IV.6 Sets, Functions, and Relati
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A-9We sometimes instead use the not
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A-11S 1 S 2S 0. . .S 3Thus, the fir
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Bibliography[Abbot] Edwin Abbott, F
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[Feller] William Feller, An Introdu
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[Programmer’s Ref.] Microsoft Pro
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composition, A-9self, 361computer a
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Kirchhoff’s Laws, 73Klein, F., 28
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at infinity, 337in projective plane
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vacuously true, A-2value, A-8Vander