Summer Workshop Series: Monte Carlo Methods for Practitioners

Mastering the Art of Stochastic Simulation

The Las Vegas Institute of Probability Theory is pleased to offer its annual Summer Workshop Series, focusing this year on Advanced Monte Carlo Methods for Practitioners. Running over four consecutive weekends in July 2024, this intensive program is designed for professionals and graduate students who need to implement robust, efficient stochastic simulations in their work. The workshop moves swiftly from foundational concepts to cutting-edge variance reduction and quasi-Monte Carlo techniques, all taught through a hands-on, code-first approach.

Workshop Curriculum and Schedule

The series is divided into four modules, each comprising a Friday evening lecture and a full-day Saturday lab session.

  • Weekend 1: Foundations and Basic Sampling: Review of probability distributions, pseudo-random number generation, the Law of Large Numbers and Central Limit Theorem in practice. Lab: Building a simple Monte Carlo integrator and pricing a European option.
  • Weekend 2: Variance Reduction Techniques: Deep dive into antithetic variates, control variates, importance sampling, and stratified sampling. Understanding the trade-offs between complexity and efficiency. Lab: Drastically improving the precision of a rare-event probability estimator for a credit default model.
  • Weekend 3: Markov Chain Monte Carlo (MCMC): Introduction to Metropolis-Hastings, Gibbs sampling, and Hamiltonian Monte Carlo. Diagnosing convergence and mixing. Lab: Bayesian inference for a hierarchical model using real-world data.
  • Weekend 4: Quasi-Monte Carlo and Applications in High Dimensions: Low-discrepancy sequences (Sobol, Halton), scrambling, and their application to problems in finance and physics. Tackling the curse of dimensionality. Lab: Using QMC for a high-dimensional path integral in quantum chemistry.

All lab sessions will be conducted in Python using libraries like NumPy, SciPy, and specialized tools such as Chaospy. Participants will receive a comprehensive digital workbook with all code examples and exercises.

Instructors and Practicalities

The workshop is led by Dr. Sam Rivera, head of our Computational Probability lab, and features guest lectures from industry experts in quantitative finance and computational physics. Class size is limited to 30 participants to ensure personalized attention during lab work.

Prerequisites: Participants should have a solid undergraduate understanding of probability and statistics, and intermediate proficiency in Python programming (familiarity with NumPy is essential). A laptop with a Python environment pre-configured is required.

Cost and Registration: The fee for the full four-weekend series is $1,200, which includes all materials, daily lunches, and a certificate of completion. A discounted rate of $900 is available for full-time students and academics. Registration opens on April 1, 2024, and is expected to fill quickly. This workshop is an unparalleled opportunity to move beyond textbook Monte Carlo and learn the practical tricks and robust implementations used by leading practitioners in the field. Transform your approach to simulation and uncertainty quantification.