Computational Statistics

Data is displayed for the academic year: 2025./2026.

Lecturers

Prof.
Zvonko Kostanjčar
PhD
Prof.
Igor Velčić
PhD
Assoc. Prof.
Tomislav Petković
PhD

Lectures

Tomislav Kovačević
PhD

Laboratory exercises

Andro Merćep
univ. mag. ing. el. techn. inf.
Tomislav Kovačević
PhD

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; and (c) visualize multidimensional data.

Study Programmes

University graduate
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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
Homeworks 0 % 10 % 0 % 10 %
Seminar/Project 50 % 25 % 50 % 25 %
Mid Term Exam: Written 0 % 30 % 0 %
Final Exam: Written 0 % 30 %
Exam: Written 50 % 60 %
Comment:

A mandatory prerequisite for a passing grade is achieving at least 50% of points on midterm and final exam combined. 5% of points are assigned to the online course on SAS.

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

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
James E. Gentle (2009.), Computational Statistics, Springer Science & Business Media

General

ID 222761
  Summer semester
5 ECTS
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
64 Good
51 Sufficient

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