# A Survey of Non-Exchangeable Priors for Bayesian Nonparametric Models

@article{Foti2015ASO, title={A Survey of Non-Exchangeable Priors for Bayesian Nonparametric Models}, author={Nicholas J. Foti and Sinead Williamson}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2015}, volume={37}, pages={359-371} }

Dependent nonparametric processes extend distributions over measures, such as the Dirichlet process and the beta process, to give distributions over collections of measures, typically indexed by values in some covariate space. Such models are appropriate priors when exchangeability assumptions do not hold, and instead we want our model to vary fluidly with some set of covariates. Since the concept of dependent nonparametric processes was formalized by MacEachern, there have been a number of…

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