This workshop will focus on the methodological and theoretical foundations of Markov Chain Monte Carlo (MCMC) methods, which are central to modern computational statistics, Bayesian inference, and a broad range of applications in physics, engineering, and data science. MCMC algorithms enable sampling from complex, high-dimensional probability distributions and are indispensable in situations where direct analytic or deterministic computational methods fail.
In recent years, the field has seen rapid developments aimed at improving the efficiency and robustness of MCMC techniques. These include advances in sampling from high-dimensional spaces, dealing with multimodal or non-smooth target distributions, and understanding the convergence properties and computational complexity of various algorithms. Such challenges are critical in emerging applications across machine learning, inverse problems, and computational physics.
The goal of the workshop is to bring together leading international experts as well as early-career researchers working at the forefront of MCMC methodology. By fostering discussion and exchange of ideas, the workshop aims to advance understanding of fundamental issues, stimulate new collaborations, and identify promising directions for future research. The program will include invited talks, contributed presentations, and ample time for informal interaction, with the intent of nurturing a dynamic and inclusive research community around MCMC methods.
For registration and detailed program please visit the event website: https://www.kth.se/mcm-sthlm2026
