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.
By Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee
SelectPFN is a prior‑data‑fitted network that jointly performs Bayesian variable selection and prediction for sparse nonlinear regression. In a single forward pass it outputs posterior inclusion probabilities for each variable and makes predictions using only sampled subsets, ensuring excluded variables cannot influence the result. The method achieves exact finite‑sample false discovery rate control via column‑permutation equivariance and knockoff statistics, and outperforms lasso, BART, DART, LassoNet, and other sparse neural networks on synthetic and real data with up to 500 predictors.
By Bin Luo
The paper introduces Amortized Factor Inference Networks (AFINs), a single inference network that can generalize across varying priors, likelihoods, latent dimensions, and observation counts. AFINs represent a model as a list of typed prior and likelihood factors and use an encode–merge–decode architecture to produce a Gaussian posterior approximation, with learned parameters independent of latent dimensionality or observation count. Experiments on synthetic and real datasets demonstrate that a single trained AFIN achieves competitive posterior approximations at significantly lower test‑time cost than NUTS and per‑task variational inference, with optional importance sampling further improving accuracy.
By Joohwan Ko, Justin Domke
arXiv:2609.38902v1 Announce Type: cross
Abstract: Augmenting small concurrent studies with external or historical cohorts is attractive in drug development, where enrollment is slow, follow-up is exp...
By Chin-Hung Huang, JooChul Lee, Huan He
arXiv:2609.24422v1 Announce Type: new
Abstract: Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian...
By Alex Kipnis, Marcel Binz, Eric Schulz
Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian inference for GLMMs yields calibrated uncertainty...