Course Archives Theoretical Statistics and Mathematics Unit
Course: Markov Chains
Level: Postgraduate
Time: Currently not offered
Syllabus
Past Exams


Syllabus:

i) Finite State Markov Chains. Examples, Classification of States, Stationary Distribution.
ii) Random walk on Finite Groups. Connection to electrical networks. Recurrence and Transience of Random walks.
iii) Branching chain, progeny distribution and progeny generating function, extinc tion probability, geometric growth in the super-critical case.
iv) Rates of convergence to stationarity, Dirichlet Form and Spectral gap methods, Some Coupling methods with applications, Cheegers inequality.
v) Poisson Processes, Continuous time Markov Chains, Birth-and-death processes.

Suggested Texts :
(a) S. M. Ross, Stochastic processes, John Wiley (1996).
(b) R. N. Bhattacharya and E. C. Waymire, Stochastic processes with applications.
(c) E. Gine, R. Grimmett and L. Saloff-Coste, Lectures on probability theory and statistics, Springer-Verlag (1997).
(d) L. Levine, Y. Peres and E. Wilmer: Markov Chains and Mixing times.
(e) D. Aldous and J. A. Fill: Reversible Markov Chains and Random Walk on Graphs.

Evaluation:
Midterm Exam 10 marks
Home Work / Assignment 40 marks
Final Exam 50 marks
Total 100 marks

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Past Exams
Midterm
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