Course

MATH-414: Stochastic simulation

Summary

The student who follows this course will get acquainted with computational tools used to analyze systems with uncertainty arising in engineering, physics, chemistry, and economics. Focus will be on sampling methods as Monte Carlo, quasi Monte Carlo, Markov Chain Monte Carlo.

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/stochastic-simulation-MATH-414

Moodle Page

http://go.epfl.ch/MATH-414

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Summary

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/stochastic-simulation-MATH-414

Moodle Page

http://go.epfl.ch/MATH-414

About this result

Lectures in this course (48)

Stochastic Simulation: Fundamentals

Introduces stochastic simulation fundamentals, covering course organization, queueing models, finance, statistics, physics, and exam details.

Random Number Generators: Properties and Examples

Covers uniform and pseudo-random number generators, properties, examples, and quality assessment through empirical tests.

Acceptance-Rejection Methods: Advanced Techniques

Explores advanced techniques in acceptance-rejection methods and sampling from normal distributions.

Acceptance-Rejection Methods: Advanced Techniques

Explores advanced Acceptance-Rejection methods, sampling from normal distribution, and multivariate random variable generation.

Gaussian Random Vectors: Conditional Generation

Explores generating Gaussian random vectors with specific components based on observed values and explains the concept of positive definite covariance functions in Gaussian processes.

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