THANKS TO ALL!
Markov Chain Monte Carlo (MCMC) algorithms are one of the most frequently used algorithms in scientific computation. They sample from a target probability distribution, possibly in spaces of very high dimension, and typically are closely related to numerical integrators because they use proposals based on the numerical simulation of auxiliary dynamics. This is especially true for the Hamiltonian Monte Carlo (HMC) method, in which an auxiliary Hamiltonian system is integrated numerically to generate the proposals. Since the resulting Hamiltonian can be decomposed into two parts, splitting methods for the numerical integration of differential equations constitute a natural option in this setting.
The aim of this workshop is to provide a platform for presenting and discussing new results in both areas and their mutual interactions, with special emphasis on applications.
The aim of this workshop is to provide a platform for presenting and discussing new results in both areas and their mutual interactions, with special emphasis on applications.