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| Detailed information |
| Original study plan |
Master's programme Computational Mathematics 2025W |
| Learning Outcomes |
Competences |
| Students are acquainted with the fundamental principles of statistical methods and the key techniques required for analysing data and making statistical inferences.
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| Skills |
Knowledge |
- Understand the concept of statistical models and apply the principles of sufficient statistics and related theorems.
- Find point estimators (moment estimator and maximum likelihood estimator) and evaluate their properties (unbiasedness, efficiency, sufficiency, and consistency).
- Calculate and interpret Fisher information.
- Investigate the quality of an estimator in the finite and asymptotic case.
- Perform interval estimation and conduct hypothesis testing and understand its implications (likelihood ratio test).
- Evaluate hypothesis tests (controlling type I/II errors, most powerful tests, p-values).
- Derive confidence intervals using different methods and evaluate their properties.
- Interpret and communicate statistical results effectively.
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Fundamental concepts of probability, statistical inference, hypothesis testing, key theorems and techniques in sufficient statistics, point estimation, and interval estimation, application of statistical methods in real-world scenarios.
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| Criteria for evaluation |
Oral exam
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| Methods |
Blackboard presentation, supported by lecture slides.
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| Language |
English |
| Study material |
- Statistical Inference, G. Casella and R. L. Berger
- Introduction to the theory of Statistical Inference, H. Liero and S. Zwanzig
- Introductory Statistics, S. M. Ross
- Introduction to Probability and Statistics for Engineers and Scientists, S. M. Ross.
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| Changing subject? |
No |
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
402STMESTMV22: VO Statistical Methods (2022W-2023S)
TMAPBVOSTAT: VO Statistical methods (2003W-2022S)
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