$γ$-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications. However, ABC can be sensitive to outliers if a data discrepancy measure is chosen inappropriately. In this paper, we propose to use a nearest-neighbor-based $\gamma$-divergence estimator as a data discrepancy measure. We show that our estimator possesses a suitable theoretical robustness property called the redescending property. In addition, our estimator enjoys various desirable properties such as high flexibility, asymptotic unbiasedness, almost sure convergence, and linear-time computational complexity. Through experiments, we demonstrate that our method achieves significantly higher robustness than existing discrepancy measures.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference
Effective uncertainty quantification is important for training modern predictive models with limited data, enhancing both accuracy and robustness. While Bayesian methods are effective for this purpose, they can be challe…
Bayesian InferenceModel-based Reinforcement LearningUncertainty QuantificationVariational Refinement for Importance SamplingUsing the Forward Kullback-Leibler Divergence
Variational Inference (VI) is a popular alternative to asymptotically exact sampling in Bayesian inference. Its main workhorse is optimization over a reverse Kullback-Leibler divergence (RKL), which typically underestim…
Bayesian InferenceVariational InferenceRobust Bayesian Inference for Moving Horizon Estimation
The accuracy of moving horizon estimation (MHE) suffers significantly in the presence of measurement outliers. Existing methods address this issue by treating measurements leading to large MHE cost function values as out…
Bayesian InferenceCombinatorial OptimizationFunctional Variational Bayesian Neural Networks
Variational Bayesian neural networks (BNNs) perform variational inference over weights, but it is difficult to specify meaningful priors and approximate posteriors in a high-dimensional weight space. We introduce functio…
Bayesian InferenceGaussian ProcessesVariational InferenceVariational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
Variational Inference (VI) is a popular alternative to asymptotically exact sampling in Bayesian inference. Its main workhorse is optimization over a reverse Kullback-Leibler divergence (RKL), which typically underestima…
Bayesian InferenceVariational Inference