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MODULE DESCRIPTOR
Module Title
Simulation and Modeling Techniques
Reference CM2605 Version 2
Created February 2024 SCQF Level SCQF 8
Approved July 2020 SCQF Points 15
Amended April 2024 ECTS Points 7.5

Aims of Module
To introduce the process of designing models of existing or proposed real-world systems and how to use these models to perform simulations that allow for predictions about the future behavior of the system. Students will also gain a solid understanding of probabilistic data modeling, interpretation, and analysis for solving practical problems more generally in computer science, business and finance, economics and engineering, and daily life.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Adapt probabilistic modelling techniques and stochastic processes for simulation problems.
2 Use probability concepts with respect to univariate and multivariate distributions for solving real world problems.
3 Compare appropriate techniques for random number generation to simulate real-world processes.
4 Write an optimised mathematical model with a computational tool for outcome prediction.

Indicative Module Content
Importance of modeling, real-world examples of modeling problems, modeling process, model equations, estimation of model parameters, computational modeling using suitable tools, central limit theorem, Chebyshev inequality, exponential, Gamma and Beta distributions with applications, Multivariate normal probability distribution, variance-covariance matrices, sampling distribution, Generating random variables, Markov, HMM, Monte Carlo method and stochastic processes.

Module Delivery
The module will be delivered through a combination of lectures, tutorials and hands-on laboratory sessions.

Indicative Student Workload Full Time Part Time
Contact Hours 48 N/A
Non-Contact Hours 102 N/A
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL 150 N/A
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 100% Outcomes Assessed: 1, 2, 3, 4
Description: Individual Coursework covering all learning outcomes.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in C1.
B The student needs to achieve a B in C1.
C The student needs to achieve a C in C1.
D The student needs to achieve a D in C1.
E The student needs to achieve an E in C1.
F The student needs to achieve an F in C1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module CM1606 and CM1601 or equivalents.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 Jain, R. 2015. Art of Computer Systems Performance Analysis: Techniques for Experimental Design Measurements Simulation and Modeling. 2nd ed. Wiley.
2 Van Landeghem, D. 2000. Simulation and Modelling: Enablers for a Better Quality of life. SCS Europe.
3 Zeigler B, Muzy A, Kofman E, (2018): Theory of Modeling and Simulation: Discrete Event & Iterative System Computational Foundations 3rd Edition.
4 Zhang L, Bernard P, Zeigler and Yuanjun L (2019) Model Engineering for Simulation.
5 Tolk T., Oren T. (2017) The Profession of Modeling and Simulation: Discipline, Ethics, Education, Vocation, Societies, and Economics.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781