Hugging Face Trending Papers
Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure
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arXiv Statistics ML
3d ago
Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure
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...
arXiv Machine Learning
Aug 31
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
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.
arXiv Machine Learning
Jul 17
Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.
arXiv Machine Learning
Sep 1
Delta-AI: Local objectives for amortized inference in sparse graphical models
arXiv:2310.02423v3 Announce Type: replace Abstract: We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\D...
arXiv Statistics ML
Aug 25
Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
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...
arXiv AI
Jul 22
RAMP: Recognition parametrisation by Amortised Message Passing
arXiv:2607. 18883v1 Announce Type: cross Abstract: A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations.