arXiv:2603.27864v1 Announce Type: cross
Abstract: We review recently proposed Bayesian approaches for clustering high-dimensional data. After identifying the main limitations of available approaches, we introduce an alternative framework based on vert…
arXiv:2510.03587v2 Announce Type: replace-cross
Abstract: Bayesian inference for doubly-intractable pairwise exponential graphical models typically involves variations of the exchange algorithm or approximate Markov chain Monte Carlo (MCMC) samplers. …
arXiv:2510.27643v2 Announce Type: replace
Abstract: This paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Baye…
arXiv:2603.23688v1 Announce Type: cross
Abstract: Background: Determining an adequate sample size is essential for developing reliable and generalisable clinical prediction models, yet practical guidance on selecting appropriate methods remains limite…