Bayesian Statistics

Sensitivity of MCMC-based analyses to small-data removal
If the conclusion of a data analysis is sensitive to dropping very few data points, that conclusion might hinge on the particular data …
Tin Nguyen, Ryan Giordano, Rachael Meager, Tamara Broderick
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Using gradients to check sensitivity of MCMC-based analyses to removing data
If the conclusion of a data analysis is sensitive to dropping very few data points, that conclusion might hinge on the particular data …
Tin Nguyen, Ryan Giordano, Rachael Meager, Tamara Broderick
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Independent finite approximations for Bayesian nonparametric inference
Completely random measures (CRMs) and their normalizations (NCRMs) offer flexible models in Bayesian nonparametrics. But their infinite …
Tin Nguyen, Jonathan Huggins, Lorenzo Masoero, Lester Mackey, Tamara Broderick
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Many processors, little time: MCMC for partitions via optimal transport couplings
Markov chain Monte Carlo (MCMC) methods are often used in clustering since they guarantee asymptotically exact expectations in the …
Tin Nguyen, Brian L. Trippe, Tamara Broderick
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Measuring the robustness of Gaussian processes to kernel choice
Gaussian processes (GPs) are used to make medical and scientific decisions, including in cardiac care and monitoring of atmospheric …
William T. Stephenson, Soumya Ghosh, Tin Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick
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Optimal transport couplings of Gibbs samplers on partitions for unbiased estimation
Computational couplings of Markov chains provide a practical route to unbiased Monte Carlo estimation that can utilize parallel …
Brian L. Trippe, Tin Nguyen, Tamara Broderick
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