Introduction to probability theory
Analysis 1 (both not mandatory)
We learn to translate real world problems into probability models. By analyzing them with tools from probability theory, we can gain valuable information about the original problem. In this course we focus on models which depend on a finite number of parameters. We learn two general principles to estimate these parameters: the method of moments and the maximum likelihood principle. Nearly as important as fitting the model is to quantify the uncertainty of our estimation. This leads to the concept of confidence intervals. To draw conclusions with high confidence, we introduce hypothesis tests.
After the course the students are able to translate real world problems into simple parametric models. Respective parameters can be fitted by either maximum likelihood estimation or moment estimators. Students can quantify the uncertainty of the estimations by bias, variance and confidence intervals. By constructing hypothesis tests, conclusions can be drawn with a high confidence. Students are aware of the necessary simplifying assumptions to translate a problem into a managable probabilistic model and know tools to check them. After the course, students will have basic knowledge of the statistical software R.
The schedule for the course can be found on MyTimeTable.
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For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different
Mode of instruction
Lectures tutorials and homework
The final grade consists of homework (20%) and a written (retake) exam (80%). To pass the course, the grade for the (retake) exam should be at least 5 and the (unrounded) weighted average of the two partial grades at least 5.5. No minimum grade is required for the homework in order to take the exam or to pass the course. The homework counts as a practical and there is no retake for it.
Fetsje Bijma, Marianne Jonker, Aad van der Vaart: An Introduction to Mathematical Statistics. Amsterdam University Press 2017.
From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
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