Nonparametric Bayesian survival analysis using mixtures of Weibull distributions

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A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims (2017, unknown): “(2006), Kottas (2006), Cheng and Yuan (2013), and Fellingham et al. (2015). Since the normal mixture model is relatively simple, both computationally and theoretically, it would be desirable if some simple modifications thereof could be used to easily accommodate the non-negativity constraint.”
A new discrete distribution (2014, methodology): “The data have been analysed recently by Damien and Walker [59] and Kottas [60].”
Bayesian mixture modeling for spatial Poisson process intensities, with applications to extreme value analysis (2007, methodology): “The Fisher’s information based on this marginalized likelihood yields the reference prior p(γ) ∝ γ−1, which is used for all three data examples in Section 4.”
A Bayesian Nonparametric Approach to Inference for Quantile Regression (2010, methodology): “Given each posterior sample { b, αb,ψb : b = 1, . . . ,B} from p( ,α,ψ | data), it is possible to sample a posterior realization Gb from p(G | ,α,ψ) using the DP constructive definition in (2) with a truncation approximation (e.g., Kottas 2006).”