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Learning overhypotheses with hierarchical Bayesian models
Developmental Science, 2007Abstract Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired. To illustrate this claim, we develop models that
Kemp, C., Perfors, A., Tenenbaum, J.
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Bayesian Hierarchical Pointing Models
Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, 2022Hang Zhao 0005 +3 more
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Hierarchical Bayesian models of cognitive development
Biological Cybernetics, 2016This article provides an introductory overview of the state of research on Hierarchical Bayesian Modeling in cognitive development. First, a brief historical summary and a definition of hierarchies in Bayesian modeling are given. Subsequently, some model structures are described based on four examples in the literature.
Thomas Glassen, Verena Nitsch
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A hierarchical Bayesian model for pattern recognition
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012The success of automated classification hinges on the choice of the representation of the data. Much research has focused on feature extraction techniques that can identify highly informative representations of a dataset. In this paper, we adapt for the purposes of classification a hierarchical Bayesian model developed by Karklin and Lewicki to model ...
Ashwini Shikaripur Nadig, Brian Potetz
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Bayesian Hierarchical Models for Subgroup Analysis
Pharmaceutical StatisticsABSTRACT In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much ...
Yun Wang +9 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Wentao Fan, Nizar Bouguila, Yang Lin
exaly
Wentao Fan, Nizar Bouguila, Yang Lin
exaly
Bayesian Network Structure Inference with an Hierarchical Bayesian Model
2010Bayesian Networks (BNs) are applied to a wide range of applications. In the past few years great interest is dedicated to the problem of inferring the structure of BNs solely from the data. In this work we explore a probabilistic method which enables the inclusion of extra knowledge in the inference of BNs.
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Hierarchical Bayesian Approach for Model Parameter Updating in Piezoelectric Energy Harvesters
Mechanical Systems and Signal Processing, 2022Gaofeng Jia, Rafael Ruiz
exaly