Computational Statistics

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.

Learning Outcomes

  1. Distinguish between random number generators
  2. Define and design Monte Carlo experiments
  3. Apply Monte Carlo estimation
  4. Explain the principles of graphical methods in computational statistics
  5. Apply repeated sampling methods
  6. Apply statistical learning methods to real world problems

Forms of Teaching

Lectures

Lectures will take place for 3 hours a week.

Laboratory

Laboratory 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

  1. Introduction to computational statistics
  2. Random numbers generation
  3. Random numbers generation
  4. Monte Carlo estimation
  5. Monte Carlo estimation
  6. Importance sampling
  7. Importance sampling
  8. Midterm exam
  9. Resampling
  10. Resampling
  11. Graphical methods in computational statistics
  12. Graphical methods in computational statistics
  13. Monte Carlo methods and statistical learning
  14. Monte Carlo methods and statistical learning
  15. Final exam

Study Programmes

University graduate
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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

Associate Lecturers

Laboratory exercises

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

Grading System

89 Excellent
76 Very Good
63 Good
50 Sufficient