Statistical Inference for Technological Applications

5 credits

Syllabus, Master's level, 1TS325

A revised version of the syllabus is available.
Education cycle
Second cycle
Main field(s) of study and in-depth level
Bioinformatics A1N, Industrial Engineering and Management A1N, Technology A1N
Grading system
Pass with distinction, Pass with credit, Pass, Fail
Finalised by
The Faculty Board of Science and Technology, 2 March 2022
Responsible department
Department of Civil and Industrial Engineering

Entry requirements

120 credits, of which 60 credits in science/engineering. Participation in courses of 25 credits in mathematics/statistics/scientific computing, of which 15 credits must be completed. Among these 25 credits 5 credits in statistics and 5 credits in computational methods should be included. Participation in a course in computer programming of 5 credits. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course the student shall be able to

  • generate simulated univariate, multivariate and temporal data sets relevant to applications within industrial analytics, bioinformatics and biotechnology,
  • apply computer intensive statistical inference methods based on resampling and Bayesian based methods for analysis of one and many variables, as alternatives to frequentistic methods, for problems relevant in industrial analytics, bioinformatics and biotechnology,
  • explain pros and cons regarding data modelling and algorithmic modelling (machine learning), respectively, for statistical inference problems,
  • explain about experimental design methods including active machine learning for problems relevant in industrial analytics, bioinformatics and biotechnology. 


Statistical inference in a technological perspective. Simulation of data from urn models, mixtures of probability distributions, multivariate distributions, linear and nonlinear regression models, Markov models, Hidden Markov Models, including additive/multiplicative experimental noise. Resampling based statistical inference in one and several variables for interval estimation and hypothesis testing. Bayesian inference in one and several variables for parameter estimation and confidence intervals, model family selection, as well as prediction. Practical and concetural comparison of data modelling with algorithm modelling (machine learning) approaches to statistical inference. Experimental design methodologies including optimal experimental design, practical approximations, and active machine learning.


Lectures, seminars and laboratory sessions.


Written examination (2 credits), and also active participation in seminars and presentation of laboratory work (3 cr).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding targeted pedagogical support from the disability coordinator of the university.

Other directives

The course may not be included in the same degree as 1TS322 Statistical Inference for Industrial Analytics or 1MB459 Statistical Inference for Bioinformatics.

No reading list found.