A General Method for Robust Bayesian Modeling
Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case basis; examples include robust linear regression, robust mixture models, and bursty topic models. In this paper we develop a general approach to robust Bayesian modeling. We show how to turn an existing Bayesian model into a robust model, and then develop a generic strategy for computing with it. We use our method to study robust variants of several models, including linear regression, Poisson regression, logistic regression, and probabilistic topic models. We discuss the connections between our methods and existing approaches, especially empirical Bayes and James-Stein estimation.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionTopic ModelsSimilar Papers 제목 키워드 기반
Building Object-based Causal Programs for Human-like Generalization
We present a novel task that measures how people generalize objects' causal powers based on observing a single (Experiment 1) or a few (Experiment 2) causal interactions between object pairs. We propose a computational m…
NavigateObjectStreaming Bayesian Modeling for predicting Fat-Tailed Customer Lifetime Value
We develop an online learning MCMC approach applicable for hierarchical bayesian models and GLMS. We also develop a fat-tailed LTV model that generalizes over several kinds of fat and thin tails. We demonstrate both deve…
Adaptive Nonparametric Perturbations of Parametric Bayesian Models
Parametric Bayesian modeling offers a powerful and flexible toolbox for scientific data analysis. Yet the model, however detailed, may still be wrong, and this can make inferences untrustworthy. In this paper we study no…
Bayesian InferenceCausal InferenceBayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability
Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits their real-world applicability. Recent wo…
Cardiac SegmentationDomain GeneralizationImage SegmentationMedical Image Segmentation+2Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind
When inferring the goals that others are trying to achieve, people intuitively understand that others might make mistakes along the way. This is crucial for activities such as teaching, offering assistance, and deciding …
Game of Chess