Geometrically constrained stochastic processes are commonly used as models in image processing and shape analysis, chemistry, robotics, and fluid dynamics. The construction, analysis, and simulation of such models involve the application of techniques and insights in probability and statistics, stochastic analysis, geometry, and numerical methods. The goal of this workshop…

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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…

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