Fast robustness quantification with variational Bayes
Bayesian hierarchical models are increasing popular in economics. When using hierarchical models, it is useful not only to calculate posterior expectations, but also to measure the robustness of these expectations to reasonable alternative prior choices. We use variational Bayes and linear response methods to provide fast, accurate posterior means and robustness measures with an application to measuring the effectiveness of microcredit in the developing world.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Stein Variational Newton Neural Network Ensembles
Deep neural network ensembles are powerful tools for uncertainty quantification, which have recently been re-interpreted from a Bayesian perspective. However, current methods inadequately leverage second-order informatio…
Bayesian InferenceUncertainty QuantificationAlpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification
We introduce a novel deep operator network (DeepONet) framework that incorporates generalised variational inference (GVI) using R\'enyi's $\alpha$-divergence to learn complex operators while quantifying uncertainty. By i…
Operator learningUncertainty QuantificationVariational InferenceSpatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer
Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large number of distribution parameters. In thi…
Adversarial RobustnessUncertainty QuantificationVariational InferenceBayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation
Medical time series datasets feature missing values that need data imputation methods, however, conventional machine learning models fall short due to a lack of uncertainty quantification in predictions. Among these mode…
EEGElectromyography (EMG)ImputationMissing Values+3Variational bagging: a robust approach for Bayesian uncertainty quantification
Variational Bayes methods are popular due to their computational efficiency and adaptability to diverse applications. In specifying the variational family, mean-field classes are commonly used, which enables efficient al…
Computational Efficiency