Two new papers introduce Simulation-Based Empirical Bayes (SBEB), a method for performing simultaneous inference across related latent variables when the likelihood is only available through a simulator. The first paper, "Simulation-Based Empirical Bayes," details SBEB's approach to computing EB estimates without an explicit density by using observed data, simulator samples, and an amortized inference network. The second paper, "An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning," provides an overview of Bayesian and frequentist frameworks for machine learning-based SBI, highlighting their application to parameter estimation, empirical Bayes, and unfolding tasks. AI
IMPACT Introduces new methods for scientific inference using machine learning, potentially improving accuracy in complex simulations.
RANK_REASON Two academic papers published on arXiv introducing and explaining Simulation-Based Empirical Bayes.
- alphaXiv
- arXiv
- Bayesian
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- machine learning
- Neural Likelihood Estimation
- Neural Posterior Estimation
- ScienceCast
- Simulation-Based Empirical Bayes
- Simulation-Based Inference
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