Stick-breaking autoregressive processes

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Bayesian forecasting in economics and finance: A modern review (2022, methodology): “Other approaches to time dependence in Bayesian nonparametrics for finance include Griffin and Steel (2011), who introduce a time-dependent stick breaking process in a general setting and develop an SV model for returns. More recently, Sun et al. (2020) use a weighted DPM to forecast return distribu”
On the stick-breaking representation of normalized inverse Gaussian priors (2012, background): “Important contributions to this area include, among others, [3, 4, 5, 6, 10, 11, 27].”
The Dependent Dirichlet Process and Related Models (2020, methodology): “More recently, similar ideas are used by Griffin and Steel (2011) to construct a family of prior distributions for a sequence of time dependent general RPMs that include the DDP setting as a special case.”
Bayesian semiparametric modeling of realized covariance matrices (2016, methodology): “Extensions to the DPM model, featuring nonparametric dependence in time or through a covariate are pursued in Griffin & Steel (2006), Griffin & Steel (2011), Rodriguez & Dunson (2011) and Jensen & Maheu (2013b).”