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Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions.
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Empirical Bayes methods
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Generalized Linear Models
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Bayesian theory
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Robust Statistics
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Finite mixture models
McLachlan, G. and Peel, D. (2000) · 2000
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“Latent Dirichlet Allocation.”
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Large-scale inference: empirical Bayes methods for estimation, testing, and prediction
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“A Sticky HDP-HMM with Application to Speaker Diarization.”
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Regression analysis of count data
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Machine Learning: A Probabilistic Approach
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“Robust Logistic Regression using Shift Parameters.”
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“Variational inference in nonconjugate models.”
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“Petuum: A New Platform for Distributed Machine Learning on Big Data.”
Xing, E. P., Ho, Q., Dai, W., Kim, J. K., Wei, J., Lee, S., Zheng, X., Xie, P., Kumar, A., and Yu, Y. (2013) · 2013
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“Robust Logistic Regression and Classification.”
Feng, J., Xu, H., Mannor, S., and Yan, S. (2014) · 2014
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“The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.”
Hoffman, M. D. and Gelman, A. (2014) · 2014
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“Black box variational inference.”
Ranganath, R., Gerrish, S., and Blei, D. (2014) · 2014
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“A New Approach to Probabilistic Programming Inference.”
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