Jupyter Notebooks
The course notebooks provide executable examples of Monte Carlo and quasi-Monte Carlo concepts, algorithms, applications, and computational performance. They are intended to complement the lectures and give students code that can be explored and adapted.
Notebooks will be organized by topic. Links will be added after each notebook has been migrated to the Fall 2026 repository, updated for the current course environment, and validated from a clean setup.
Notebook content is being migrated from the previous course offering. Each notebook is linked here only after it has been updated and validated in the Fall 2026 course environment.
Sampling
Notebooks on random and low discrepancy sample generation, acceptance-rejection sampling, conditional Monte Carlo, Markov chain Monte Carlo, and related sampling methods will appear here.
- Generating Samples โ construct and transform IID and low discrepancy samples, including binomial, zero-inflated exponential, Gaussian-process, Brownian-motion, and financial-option examples
Applications
- Are We There Yet? โ use a travel-time model to explore Monte Carlo convergence, error assessment, quantiles, conditional Monte Carlo, and randomized Sobol sampling
Additional notebooks will apply Monte Carlo and quasi-Monte Carlo methods to integration, financial options, queueing models, and other examples.
Performance
Notebooks examining convergence, discrepancy, accuracy, algorithmic efficiency, and computational performance will appear here.