arXiv:2607. 04650v1 Announce Type: cross Abstract: Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially with network size.
By Pei Heng, Xinyi Hu, Yi Sun
arXiv:2606. 27737v1 Announce Type: new Abstract: Programming adaptive behaviors at the cellular level is a long-standing goal that raises the question of how probabilistic computation can be implemented in biochemical systems.
By Mauricio Montes, Gregoire Sergeant-Perthuis
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
By Yichen Gu, Yuxuan Song, Weizhou Qian, Yixin Wang, Joshua Welch
arXiv:2607. 19126v1 Announce Type: cross Abstract: Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model.
By Peter Jung, Giuseppe Marra, Ondrej Kuzelka
arXiv:2608. 04930v1 Announce Type: cross Abstract: Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data.
By Shrenik Zinage
arXiv:2603. 11946v2 Announce Type: replace-cross Abstract: Probabilistic circuits (PCs) enable exact and tractable inference but employ data independent mixture weights that limit their ability to capture local geometry of the data manifold.
By Sahil Sidheekh, Sriraam Natarajan