Ruben Seyer
My focus lies at the intersection of Bayesian inference and machine learning, where we develop computational methods for statistics. I am interested in Markov Monte Carlo methods and applications to spatial statistics and point processes. Among other things, my research concerns designing non-reversible samplers, and applying stochastic gradient methods to MCMC and piecewise deterministic Markov processes to automatically turn samplers into gradient samplers.
Showing 1 publications
Differentiating Metropolis-Hastings to Optimize Intractable Densities
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Showing 2 research projects
Fast Bayesian Inference with Piecewise Deterministic Markov Processes
Inference in the face of intractability: Bayesian applications of continuous-time Markov processes