Bayesian Methods, 7.5 ECTS

Second level

Facts

Study pace 50%
Study time Daytime
Study form Normal
Language English
Special eligibility requirements
Prerequisites for the course are courses equivalent to 60 ECTS in mathematical statistics, including courses corresponding to Theory of Statistical Inference (MT5003) and Linear Statistical Models (MT5001). Also required is knowledge equivalent to Swedish upper secondary course English B.

Description

The course covers Bayes’ formula, informative and non-informative prior distributions, posterior distributions, single- and multiparameter distributions like binomial, multinomial och normal distributions, hierarchical models, linear models, Bay…

The course covers Bayes’ formula, informative and non-informative prior distributions, posterior distributions, single- and multiparameter distributions like binomial, multinomial och normal distributions, hierarchical models, linear models, Bayesian inference and goodness-of-fit measures and stochastic simulation with MCMC (Markov Chain Monte Carlo).

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Area of interests: Science and Mathematics

Science and mathematics help us understand how the world around us is connected – from the origin and structure of the universe, to the development and function of humanity and all other organisms on earth. Scientific knowledge makes it possible …

Science and mathematics help us understand how the world around us is connected – from the origin and structure of the universe, to the development and function of humanity and all other organisms on earth. Scientific knowledge makes it possible to critically examine the credibility of information in different areas of everyday life, society, and the media. As a scientist or mathematician, you will be attractive on a large job market that covers all parts of society and includes everything from pure technology companies to environment and healthcare, as well as research.

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Subject

Mathematical Statistics

Mathematical statistics is the subject within applied mathematics that describes and analyses random events.

The foundation is the mathematical probability theory, which goes back to the 17th century, but has in its modern form developed mostly during the 20th century. Probability theory is also the foundation of statistical theory on how to draw conclusions from data with random features. The rise of computer technology has also significantly contributed to broadening the field of applications within mathematical statistics. Today is mathematical statistics one of the most powerful tools in applied mathematics.

Examples that apply mathematical statistics to a large extent are the insurance and finance sectors, biological and medical research (biostatistics) and industrial applications like telecommunications and quality control.

The department cooperates closely both in research and teaching with the pharmaceutical industry, medical institutions and banking and insurance companies. Depending on direction of studies, a student with an exam in mathematical statistics has a good chance to get employment in these sectors.

Mathematical statistics can be studied in elective courses, as a major in a Bachelor’s or Master’s degree or as a complementary subject. Courses cover both theory and applications in various areas. Mathematical statistics is included in the Bachelor’s programmes in Mathematics, Biomathematics and Mathematics and Economics and a major in the Master’s programmes in Mathematical statistics, Biostatistics, Finance mathematics and Finance and Actuarial science.

Mathematical Statistics