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HU Credits:
2
Degree/Cycle:
2nd degree (Master)
Responsible Department:
Statistics
Semester:
1st Semester
Teaching Languages:
Hebrew
Campus:
Mt. Scopus
Course/Module Coordinator:
Yosef Rinott
Coordinator Office Hours:
Monday 11-12
Teaching Staff:
Prof Yosef Rinott
Course/Module description:
Information Theory and applications in Statistics, Coding and related areas.
Course/Module aims:
Understand basic notions of information, information transfer, and related notions in statistics.
Learning outcomes - On successful completion of this module, students should be able to:
Know basic ideas in information. Understand the relation to statistical notions such as sample size, sufficiency, testint hypotheses, power of tests, etc.
Attendance requirements(%):
100%
Teaching arrangement and method of instruction:
Lecture and homework exercises.
Course/Module Content:
Entropy, Kullback-Leibler divergence, Data-Processing Inequality, Sufficiency and related inequalities and their relation to statistics: power of tests, data compression etc.
Asymptotic Equipartition Property, Data Compression and entropy
Entropy Rates of a Stochastic Process
Concentration inequalities – Large Deviation theory with applications to information and statistics
Data compression and coding.
Information Theory and Statistics: estimation, Hypothesis Testing, Bahadur efficiency, Fisher information.
Data Privacy.
Required Reading:
Elements of Information Theory, 2nd Edition
Thomas M. Cover, Joy A. Thomas
Lecture Notes for Statistics 311/Electrical Engineering 377
John Duchi
March 13, 2019
Additional Reading Material:
Information Theory and
Statistics: A Tutorial
Imre Csisz´ar and Paul C. Shields
Course/Module evaluation:
End of year written/oral examination 85 %
Presentation 0 %
Participation in Tutorials 5 %
Project work 0 %
Assignments 10 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
All reading material can be downloaded legally.
The first lecture will take place on November 4.
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