Course information

  • Title: Scientific Modelling Computer Lab
  • Neptun code:
  • Instructor: Péter Pollner
  • Semester: 4
  • Type: Computer Lab
  • Credit points: 5
  • Prerequisites: -

Course description

The course is aimed to provide advanced theoretical and practical competences for proactive scientific modelling and project management. Students work on small projects individually and on a large projects throughout the semester in groups. The projects aim computer simulations and models for solving actual problems in various disciplines and scientific fields. By working on the projects and discussing issues in class, students get acquainted with problem setting, with abstracting down to simple models and with suitable programming skills. The projects end with a presentation, where students learn disseminate results and conclude a project.


  • Foundation of modeling: abstraction, dimension reduction, measurability hypothesis testing
  • Markov models
  • Agent based models
  • Models of network science
  • Dynamical systems, nonlinear differential equations
  • Monte carlo methods
  • Time series analysis
  • Data embedded in space
  • Working with discrete data
  • Non-stationary data: measuring, recording, online (stream) analysis
  • Error finding, validation of models, handling biased data and extreme values
  • Local regression, krigging

Recommended readings

  • Walter Zucchini, Iain L. MacDonald: Hidden Markov Models for Time Series: An Introduction Using R
  • Cappé, Olivier, Moulines, Eric, Ryden, Tobias : Inference in Hidden Markov Models
  • Steven F. Railsback : Agent-Based and Individual-Based Modeling: A Practical Introduction
  • Allen B. Downey : Think Complexity: Complexity Science and Computational Modeling