Math Problem Statement

If (X_n) is a Markov process with two states, say 0 and 1, and the transition matrix is P = ((1/2, 1/2), (1/4, 3/4)), find the eigenvalues of P. Show that the π satisfying π = πP exists and find it. Run the Markov process multiple times and check the convergence of q^(n) = q^(0) P^n to π.

Solution

Let's solve the problem step by step:

1. Find the Eigenvalues of PP:

The transition matrix PP is:

P=(12121434)P = \begin{pmatrix} \frac{1}{2} & \frac{1}{2} \\ \frac{1}{4} & \frac{3}{4} \end{pmatrix}

The eigenvalues λ\lambda are obtained by solving det(PλI)=0\det(P - \lambda I) = 0:

det((12λ121434λ))=0\det \left( \begin{pmatrix} \frac{1}{2} - \lambda & \frac{1}{2} \\ \frac{1}{4} & \frac{3}{4} - \lambda \end{pmatrix} \right) = 0

det((12λ121434λ))=(12λ)(34λ)18=0\det \left( \begin{pmatrix} \frac{1}{2} - \lambda & \frac{1}{2} \\ \frac{1}{4} & \frac{3}{4} - \lambda \end{pmatrix} \right) = \left( \frac{1}{2} - \lambda \right) \left( \frac{3}{4} - \lambda \right) - \frac{1}{8} = 0

Simplify:

(12λ)(34λ)=3854λ+λ2\left( \frac{1}{2} - \lambda \right) \left( \frac{3}{4} - \lambda \right) = \frac{3}{8} - \frac{5}{4}\lambda + \lambda^2

λ254λ+12=0\lambda^2 - \frac{5}{4}\lambda + \frac{1}{2} = 0

Solve using the quadratic formula:

λ=b±b24ac2a,a=1,b=54,c=12\lambda = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}, \quad a = 1, \, b = -\frac{5}{4}, \, c = \frac{1}{2}

λ=(54)±(54)24(1)(12)2(1)\lambda = \frac{-(-\frac{5}{4}) \pm \sqrt{\left(-\frac{5}{4}\right)^2 - 4(1)\left(\frac{1}{2}\right)}}{2(1)}

λ=54±251622\lambda = \frac{\frac{5}{4} \pm \sqrt{\frac{25}{16} - 2}}{2}

λ=54±251632162\lambda = \frac{\frac{5}{4} \pm \sqrt{\frac{25}{16} - \frac{32}{16}}}{2}

λ=54±7162\lambda = \frac{\frac{5}{4} \pm \sqrt{-\frac{7}{16}}}{2}

So, the eigenvalues are ( \lambda

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Math Problem Analysis

Mathematical Concepts

Markov Chains
Eigenvalues and Eigenvectors
Stationary Distribution

Formulas

Eigenvalue equation: det(P - λI) = 0
Stationary distribution equation: πP = π
Convergence: q^(n) = q^(0)P^n

Theorems

Eigenvalue calculation
Markov Chain Stationary Distribution Theorem
Law of Large Numbers

Suitable Grade Level

University Level (Advanced Probability or Stochastic Processes)