# Evaluating Sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics

@article{Liu2018EvaluatingST, title={Evaluating Sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics}, author={Runjing Liu and R. Giordano and Michael I. Jordan and T. Broderick}, journal={arXiv: Methodology}, year={2018} }

A central question in many probabilistic clustering problems is how many distinct clusters are present in a particular dataset. A Bayesian nonparametric (BNP) model addresses this question by placing a generative process on cluster assignment. However, like all Bayesian approaches, BNP requires the specification of a prior. In practice, it is important to quantitatively establish that the prior is not too informative, particularly when the particular form of the prior is chosen for mathematical… CONTINUE READING

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