Computational Statistics
Data is displayed for the academic year: 2024./2025.
Laboratory exercises
Course Description
The course aims to enable students to; (a) evaluate univariate and multivariate linear statistical methods and models using statistical simulation, (b) use repeated sampling and simulation methods to test hypotheses and estimate confidence intervals, (c) visualize multidimensional data.
Study Programmes
University graduate
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(2. semester)
Learning Outcomes
- Distinguish between random number generators
- Define and design Monte Carlo experiments
- Apply Monte Carlo estimation
- Explain the principles of graphical methods in computational statistics
- Apply repeated sampling methods
- Apply statistical learning methods to real world problems
Forms of Teaching
Lectures
Lectures will take place for 3 hours a week.
LaboratoryLaboratory exercises are organized as projects.
Grading Method
Continuous Assessment | Exam | |||||
---|---|---|---|---|---|---|
Type | Threshold | Percent of Grade | Threshold | Percent of Grade | ||
Seminar/Project | 50 % | 40 % | 50 % | 40 % | ||
Mid Term Exam: Written | 0 % | 30 % | 0 % | |||
Final Exam: Written | 0 % | 30 % | ||||
Exam: Written | 50 % | 60 % |
Comment:
Week by Week Schedule
- Introduction to computational statistics
- Random numbers generation
- Random numbers generation
- Monte Carlo estimation
- Monte Carlo estimation
- Importance sampling
- Importance sampling
- Midterm exam
- Resampling
- Resampling
- Graphical methods in computational statistics
- Graphical methods in computational statistics
- Monte Carlo methods and statistical learning
- Monte Carlo methods and statistical learning
- Final exam
Literature
James E. Gentle (2006.), Elements of Computational Statistics, Springer Science & Business Media
Efron B., T. Hastie (2016.), Computer Age Statistical Inference, Cambridge Academic Press
For students
General
ID 222761
Summer semester
5 ECTS
L1 English Level
L1 e-Learning
45 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory
0 Physical education excercises
Grading System
89 Excellent
76 Very Good
63 Good
50 Sufficient