Directed Networks
Structure Learning with Adaptive Random Neighborhood Informed MCMC
In this paper, we introduce a novel MCMC sampler, P ARNI-DAG, for a fully-Bayesian approach to the problem of structure learning under observational data.
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods Veit D. Wild
We establish the first mathematically rigorous link between Bayesian, variational Bayesian, and ensemble methods. A key step towards this is to reformulate the non-convex optimisation problem typically encountered in deep learning as a convex optimisation in the space of probability measures.
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
However, it is known that even with infinite i.i.d.
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier
However, SBC has drawbacks: The test statistic is somewhat ad-hoc, interactions are difficult to examine, multiple testing is a challenge, and the resulting p-value is not a divergence metric.