The paper introduces duet sampling, a generalization of the Metropolis-Hastings algorithm that selects two proposal distributions from a pool at each iteration to resample parents for multiple nodes in a directed acyclic graph (DAG). This approach enables Markov chain moves that mix more efficiently without extra computational cost, outperforming existing state‑of‑the‑art methods in experiments. The authors demonstrate that their novel moves, both alone and combined, achieve superior performance even when previous methods also adopt duet sampling.
By Daniele Nikzad, Mikko Koivisto
The paper introduces JSP-GFN, a Generative Flow Network that jointly infers the structure and parameters of a Bayesian Network. It sequentially generates a directed acyclic graph edge by edge and then samples the corresponding conditional probability parameters once the full structure is known. Experiments on simulated and real data show that JSP‑GFN accurately approximates the joint posterior and outperforms existing methods.
By Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Esmeralda S. Whitammer, Laurent Charlin, Yoshua Bengio
arXiv:2510.17714v3 Announce Type: replace-cross
Abstract: Novel Markov Chain Monte Carlo (MCMC) methods have enabled the generation of large ensembles of redistricting plans modeled as a graph partit...
By Atticus McWhorter, Daryl DeFord
arXiv:2509.13267v3 Announce Type: replace-cross
Abstract: A discrete Bayesian network is a directed acyclic graph (DAG) consisting of categorical variables. Two popular approaches for DBN modeling in...
By Alexander Dombowsky, David B. Dunson
arXiv:2606. 16073v1 Announce Type: new Abstract: Sampling from complex, unnormalized probability densities is a fundamental challenge in Bayesian inference and probabilistic modeling.
By Kirill Korolev, Nikita Morozov, Stepan Pavlenko, Esmeralda S. Whitammer, Sergey Samsonov
arXiv:2606. 19264v1 Announce Type: new Abstract: The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem.
By Sanghyeok Choi, Henry Gouk, Esmeralda S. Whitammer
arXiv:2609.40024v1 Announce Type: new
Abstract: We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a direc...
By Daniel Habermann, Andreas Bulling, Stefan T. Radev, Paul-Christian B\"urkner
arXiv:2608. 08644v1 Announce Type: new Abstract: We consider the problem of sampling compositional and discrete objects from a given unnormalized posterior distribution.
By Tiago da Silva, Esmeralda S. Whitammer, Salem Lahlou
We consider the problem of sampling compositional and discrete objects from a given unnormalized posterior distribution. Notably, recent studies have shown that this problem can be efficiently solved by learning a deterministic Markov Decision Process (MDP) that progressively builds each object in proportion to the posterior.
The paper introduces Particle GFlowNets, showing that Generative Marginalization Models (MaMs) are equivalent to Generative Flow Networks. It extends MaMs to non‑autoregressive sampling and proposes an automatic full‑state rejuvenation criterion based on the Gelman‑Rubin statistic to accelerate learning. Experiments demonstrate significant training speedups in large combinatorial spaces.
By Tiago da Silva, Diego Mesquita, Salem Lahlou
We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a directed acyclic graph, our method automatically deriv...
The paper introduces OSIS, a one‑step implicit sampler that uses short‑time MCMC dynamics to define a local target for an implicitly modelled distribution. In the asymptotic limit, the model converges to a fixed point of the MCMC kernel, enabling unbiased sampling from the Bayesian posterior. Experiments show OSIS outperforms multi‑step amortised samplers and non‑amortised MCMC baselines while being far more efficient at sampling time.
By Vincent Pauline, Kirill Tamogashev, Arran Carter, Sanghyeok Choi, Stefan Bauer, Esmeralda S. Whitammer