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Found 7 papers, 1 papers with code
A unified Bayesian framework for interval hypothesis testing in clinical trials
The American Statistical Association (ASA) statement on statistical significance and P-values \cite{wasserstein2016asa} cautioned statisticians against making scientific decisions solely on the basis of traditional P-values.
Differentially private Bayesian tests
We present a novel differentially private Bayesian hypotheses testing framework that arise naturally under a principled data generative mechanism, inherently maintaining the interpretability of the resulting inferences.
Constrained Reweighting of Distributions: an Optimal Transport Approach
The key idea is to ensure that the maximum entropy weight adjusted empirical distribution of the observed data is close to a pre-specified probability distribution in terms of the optimal transport metric while allowing for subtle departures.
Scalable Model-Based Gaussian Process Clustering
Gaussian process is an indispensable tool in clustering functional data, owing to it's flexibility and inherent uncertainty quantification.
Fair Clustering via Hierarchical Fair-Dirichlet Process
The advent of ML-driven decision-making and policy formation has led to an increasing focus on algorithmic fairness.
Bayesian Spike Train Inference via Non-Local Priors
Advances in neuroscience have enabled researchers to measure the activities of large numbers of neurons simultaneously in behaving animals.
Robust probabilistic inference via a constrained transport metric
The proposed approach finds applications in a wide variety of robust inference problems, where we intend to perform inference on the parameters associated with the centering distribution in presence of outliers.
