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stat.ME2026
Bayesian Posterior Learning of Mixed Graphical Models
Erdong Guo, Alexandros Beskos, Maria De Iorio
Mixed Graphical Models (MGMs) provide a flexible framework for structure learning from heterogeneous data by treating sets of both continuous and discrete. Bayesian inference for M…
stat.ME2026
Similarity-Driven Proposals for MCMC Algorithms on Discrete Spaces
Luca Aiello, Raffaele Argiento, Alexandros Beskos +1
Recent research has led to the development of MCMC algorithms with likelihood-informed proposals when targeting posterior distributions supported on discrete state spaces. Our work…