arXiv Statistics ML
Amortized Data Borrowing with Exchangeability-Aware Neural Posterior Estimation
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
Hugging Face Trending Papers
Sep 21
Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
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...
arXiv Machine Learning
Sep 22
Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
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...
arXiv Machine Learning
Jun 2
Bayesian meta-learning for modeling Alzheimer's disease progression
arXiv:2606. 02228v1 Announce Type: cross Abstract: Predicting whether an individual with Alzheimer's disease will experience mild or severe disease progression is essential for personalized treatment.
arXiv Machine Learning
Jun 15
Minimum Distance Summaries for Robust Neural Posterior Estimation
arXiv:2602. 09161v2 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) enables amortized Bayesian inference by first training a neural posterior estimator (NPE) on prior-simulator pairs, typically through low-dimensional summary statistics, which can then be cheaply reused for fast inference by querying it on new test observations.
arXiv Machine Learning
Jun 18
Shrinkage priors for Bayesian Substitute Confounders
arXiv:2606. 18535v1 Announce Type: cross Abstract: Multi-cause observational studies contain information about unmeasured confounding through the dependence structure among causes.
Hugging Face Trending Papers
Jun 14
Bayesian Networks with Latent Time Embedding for Stage-Aware Causal Modeling of Alzheimer's Disease Progression
Alzheimer's disease (AD) progression is often described through the amyloid-tau-neurodegeneration, or AT(N), cascade. However, most longitudinal models represent this cascade either as a fixed sequence of biomarkers or as a black-box forecasting task.