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Theory of Statistical Inference

This course aims to give students a solid background and understanding of the main results and methods in the theory of statistical inference.

This goal of the course is to give the student a solid background and understanding of main results and central methods of statistical inference, such as likelihood theory, sufficiency, information, asymptotics, resampling (bootstrap) and Bayesian statistics/aposteriori disitributions. These notions are applied to point estimation, interval estimation and hypothesis testing. Theoretical evaluations are completed with many examples and the students are trained to implement the methods of the course using statistical software. The course contents is an entry to more advanced courses within Probability Theory and Statistics.

  • Course structure

    The course consists of two elements, theory and computer exercises.

    Teaching format

    Instruction is given in the form of lectures, and computer exercises.


    Examination for the course is done with a written examination, and written presentation of the computer exercises.


    A list of examiners can be found on

    Exam information

  • Schedule

    The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
  • Course literature

    Note that the course literature can be changed up to two months before the start of the course.

    Held & Bové: Likelihood and Bayesian Inference. Springer.

    Older editions of this book are called Applied Statistical Inference.

    List of course literature Department of Mathematics

  • Course reports

  • More information

    Course web

    We do not use Athena, you can find our course webpages on

  • Contact