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Probability & Statistics


Richter Mendoza F. J.

Course director

Wit E. J.

Course director

Boschi M.



This course is uniquely structured around the paradigm of probabilistic simulation, integrating this foundational concept into every aspect of its curriculum. From the exploration of probability axioms and conditional probability distributions, through the study of Monte Carlo methods and stochastic simulations, to the application of advanced estimation techniques and hypothesis testing, the course continually emphasizes the relevance and utility of probabilistic simulations. Through this consistent lens, the course immerses students in the practical application of these methodologies, fostering a deeper understanding of the inherent probabilistic nature of statistical analysis and data interpretation. This simulation-based pedagogical approach equips students with the skills and tools necessary to effectively manage uncertainty, model complex scenarios, and make informed decisions in their future endeavors across various fields.


  • Learn about probability axioms and (conditional) probability distributions. 
  • Random number generation, stochastic simulation.
  • Monte-Carlo and importance sampling
  • Understand law of large numbers and central limit theorem 
  • Study estimation methods, such a method-of-moments and maximum likelihood, and ways to evaluate estimators 
  • Bootstrapping
  • Develop hypothesis testing
  • Understand and apply regression models

Teaching mode

In presence

Learning methods

Combination of lectures and tutorials

Examination information

Written exam and projects