Search Results for author:
Found 3 papers, 2 papers with code
Sensitivity-Aware Amortized Bayesian Inference
In this work, we propose sensitivity-aware amortized Bayesian inference (SA-ABI), a multifaceted approach to efficiently integrate sensitivity analyses into simulation-based inference with neural networks.
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis.
A Deep Learning Method for Comparing Bayesian Hierarchical Models
Bayesian model comparison (BMC) offers a principled approach for assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
