Syllabus for Bayesian Statistics and Data Analysis

Bayesiansk statistik och dataanalys

  • 7.5 credits
  • Course code: 2ST128
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Statistics A1F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2022-03-03
  • Established by: The Department Board
  • Applies from: week 35, 2022
  • Entry requirements: 120 credits including 90 credits in statistics
  • Responsible department: Department of Statistics

Learning outcomes

After completing the course, the student is expected to

  • have knowledge of basic concepts, philosophy and views in Bayesian statistics
  • be able to derive posterior distributions in simple cases
  • be able to derive and use predictive distributions
  • be able to identify and formulate Bayesian statistical models for analysis and prediction
  • be able to formulate and estimate models with modern computer-based methods for approximation of posterior distributions
  • understand and be able to use basic principles for decisions under uncertainty
  • have knowledge of how to use Bayesian methods for model comparisons
  • be able to independently and critically review use of Bayesian methods
  • in writing and orally present a completed statistical analysis with Bayesian methods

Content

Bayes' theorem, prior distribution, posterior distribution, predictive distributions, conjugate prior distributions, basic decision theory, Bayesian model comparison, posterior approximations, MCMC, probabilistic programming

Instruction

Instruction is given in forms of lectures, exercise sessions and computer labs

Assessment

Assessment takes place through a small data analysis project at the end of the course, as well as through mandatory hand-in assignments/tests.

Reading list

The reading list is missing. For further information, please contact the responsible department.