Risk Analysis and Management

Course guarantor:

Tomáš Janda, Jiří Šejnoha

Lecturer:

Tomáš Janda, Jiří Šejnoha

Tutor:

Supervisor:

Department of Mechanics

Synopsis:

1. Terminology

2. Theory - Probability and mathematical statistics, Reliability

3. Risk analysis/assessment

- Hazard identification

- Risk quantification

- Tools and numerical methods

- Classical statistical inference

- Bayesian inference

- Monte Carlo methods

4. Risk management

- Decision analysis

- Utility theory in decision analysis

Requirements:

The elementary knowledge of differential and integral calculus is welcomed. Basic experience with programing is also welcomed since all examples are provided in Python.

Syllabus of lectures:

1. Basic relations and terminology

2. Common probability distributions

3. Transformation of random variables

4. Reliability of simple structures

5. Processes evolving in time

6. Solution methods and models

7. Markov chains and Kolmogorov equations

8. Monte Carlo method, Latin Hypercube sampling

9. Advanced methods: Subset simulation, MCMC sampling

10. Bayesian inference, Metropolis Hastings algorithm

Syllabus of tutorials:

Hands-on exercises are integrated into lectures.

Study Objective:

Study materials:

David Vose, Risk Analysis: A Quantitative Guide, 3rd Edition, Wiley, 2008

Michael Rees, Business Risk and Simulation Modelling in Practice Using Excel VBA and RISK, Wiley, 2015

Note:

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans: