Model the uncertainties
A product can have variable performance and behaviour during its lifespan, depending on the stresses it is subjected to or its characteristics. The lack of knowledge and the imprecision of experimental measurements motivate the modelling of product-related uncertainties. The MS course introduces the fundamental elements for modelling uncertainties and analysing product performance in all cases.
- Understand the reasons behind the probabilistic approach
- Learn how to build a probabilistic model
- Be able to critically examine the results of a statistical analysis
- Basic knowledge of mathematics.
- Good knowledge of Python.
- R&D Engineer
- Design Office Engineer
- Operational Safety Engineer
- March 18-19, 2021
- April 8-9, 2021
- September 16-17, 2021
- November 25-26, 2021
18/20 Boulevard Reuilly, 75012 PARIS – Métro Dugommier
Why use randomness in modelling?
Fundamental concepts of statistics and probability
- Random variable and probability law
- Uniform and Gaussian Laws
- Expectancy and variance
- Distribution function and density
Couple of random variables, conditioning and regression
- Interactions between random variables
- Conditional probabilities
- Predicting with data: the linear model
Create a model and decide on a sample
- Model uncertainty using random variables
- Infer parameters using a sample
- Quantitatively test assumptions and make decisions
Exercises (paper – pencil or Python)catalogue_2021_incertitude
Register or ask for further information. Personalised training courses can also be provided. The location of the training is subject to change. Training courses can be held at a distance. In this case, the training can be delivered in several short sessions (e.g. 2 hours) via videoconference software, spread over several days, in agreement with the trainer and the participants.