|
HU Credits:
3
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Statistics
Semester:
1st Semester
Teaching Languages:
Hebrew
Campus:
Mt. Scopus
Course/Module Coordinator:
Or Zuk
Coordinator Office Hours:
Monday, 10:30-11:30
Teaching Staff:
Dr. Or Zuk
Course/Module description:
A course focusing on Markov Chains, their theoretical properties and classic and modern applications
Course/Module aims:
Provide a basic understanding of Markov Chains and their usage in several areas and statistical models
Learning outcomes - On successful completion of this module, students should be able to:
Understand the basic definitions of Markov chains, compute properties of states, simulate from a Markov chain, apply Markov Chains to problems in statistics
Attendance requirements(%):
0
Teaching arrangement and method of instruction:
Frontal lectures and tirgul
Course/Module Content:
Discrete-time Finite Markov Chain
Classification of states: transience and recurrence, communication, irreducibility
Periodicity, absorption, transition and hitting times and probabilities
Invariant and Stationary distribution
Mixing times
Reversibility
Markov Chain Monte Carlo, Metropolis-Hastings algorithm
Hidden Markov Models
Continuous time Markov Chains
Markov Chains on Countable State Space
Required Reading:
None
Additional Reading Material:
Norris J.K., Markov Chains
Levin, D., Peres, Y. and E. Wilmer. Markov Chains and Mixing Times.
Haggstrom, O. Finite Markov Chains and Algorithmic Applications
Grading Scheme :
Written / Oral / Practical Exam / Home Exam 100 %
Additional information:
|
