Statistics and Information Theory (2016)

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• Topics for the exam
• Course requirements
• Homework exercises
• Lecture notes: I. Csiszár, P. Shields, Information Theory and Statistics: A Tutorial
• Basic notions (Lesson 1)
• Addendum to Lesson 2
• Addendum to Lesson 3 and 5 (ML estimation in exponential family and the EM algorithm)
• Addendum to Lesson 4 (Graphical and log-linear models)
• Addendum to Lesson 7 (Universal coding and channel capacity)
• To read:
  Contents of the March 2016 issue of the journal Hungarian Science, where you can find more information about Shannon and the Hungarian school of information theory(more information)