The following Markov matrix describes a stochastic process involving 3 states 81, 82, 83. The number given by element i, j is the probability of transition from state j to state i; i.e. the column corresponds to the current state and the row corresponds to the future state. future state 9/10( A pictorial representation of the transition probabilities is below. 1/10 s1 s2 s3 s1 9/10 3/5 0 s2 1/10 3/10 1/2 0 1/10 1/2 s3 3/5 3/10 S2 1/10 1/2 S3 (i) Find the characteristic polynomial. (ii) Use the characteristic equation to solve for the second and third eigenvalues, given A₁ = 1. [Recall, a Markov matrix has max(X) = 1] ¹1/2 (iii) Find the first eigenvector, 2₁. (iv) Show that the eigenvectors ₁, ₂ = [−2,1,1], 22 and v V3 by confirming that c₁ = C₂ = c3 = 0 is the unique solution to c₁v₁ = 0. = [3,-4, 1] are linearly independent 3 i=1
The following Markov matrix describes a stochastic process involving 3 states 81, 82, 83. The number given by element i, j is the probability of transition from state j to state i; i.e. the column corresponds to the current state and the row corresponds to the future state. future state 9/10( A pictorial representation of the transition probabilities is below. 1/10 s1 s2 s3 s1 9/10 3/5 0 s2 1/10 3/10 1/2 0 1/10 1/2 s3 3/5 3/10 S2 1/10 1/2 S3 (i) Find the characteristic polynomial. (ii) Use the characteristic equation to solve for the second and third eigenvalues, given A₁ = 1. [Recall, a Markov matrix has max(X) = 1] ¹1/2 (iii) Find the first eigenvector, 2₁. (iv) Show that the eigenvectors ₁, ₂ = [−2,1,1], 22 and v V3 by confirming that c₁ = C₂ = c3 = 0 is the unique solution to c₁v₁ = 0. = [3,-4, 1] are linearly independent 3 i=1
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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