MATH1002 Lecture Notes - Lecture 4: Markov Chain, Diagonal Matrix, Linear Independence

• School: University of Sydney
• Department: Mathematics
• Course: MATH1002
• Professor: Nathan Brownlowe

Document Summary

C1v1 + c2v2 + c3v3 + + cnvn = 0. Then all the coefficients c are 0. Application of eigenvalues / eigenvectors in markov chains / leslie model. The power of a diagonal matrix is just each diagonal entry raised to the power. We can use eigenvalues of 1 to find the steady state vector for a markov chain. The probability of moving from state j to state i in k (very large) steps is the i entry in the steady state vector.