Stratification for Markov Chain Monte Carlo

Explore advanced techniques for Markov Chain Monte Carlo (MCMC) sampling in this insightful 39-minute lecture by Jonathan Weare at the Alan Turing Institute. Delve into the concept of stratification and its application to MCMC methods, a crucial tool for statistical inference in complex scientific and engineering models. Learn how this approach can enhance the efficiency and accuracy of sampling in high-dimensional spaces, particularly when dealing with large volumes of data. Gain valuable insights into the emerging paradigm of combining statistical inference, high-throughput computation, and physical laws in modern modeling approaches. Understand the challenges and potential solutions in identifying and parameterizing crucial features in complex systems across various fields, including collective dynamics, molecular modeling, cell biology, and fluid dynamics.

Jonathan Weare (DDMCS@Turing): Stratification for Markov Chain Monte Carlo