Titlebook: Nonparametric Bayesian Inference; Contributions by Jea Jean-Pierre Florens,Michel Mouchart Book 2024 The Editor(s) (if applicable) and The

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Some Useful Properties of the Dirichlet Processow how it can be used to derive interesting properties of the Dirichlet process. Secondly, it is shown that the characterization of the Dirichlet process through independence relations between associated .-fields entails a nice description of the posterior distribution of the Dirichlet process at po

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Bayes, Bootstrap, and Moments. Powerful representations of the Dirichlet processes are used and provide an efficient numerical strategy to deal with such models. The so-called noninformative prior specification gives a Bayesian interpretation of the bootstrap method, but some pathologies of this prior measure are pointed out. S

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Nonparametric Bayesian Survival Analysished by John Wiley and Sons Ltd. Most of the prior specifications proposed in the literature since Ferguson’s seminal paper are presented with a special emphasis on the class of neutral to the right processes and on its subclass introduced by Hjort under the denomination of beta processes. Berliner-H

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Simulation of Posterior Distributions in Nonparametric Censored Analysisng time. The prior probability on . is a Dirichlet process. Hjort (Ann Stat 18(3):1259–1294, 1990) shows that the posterior distribution is a neutral to the right process whose hazard function is a beta process. Lo (Ann Stat 21(1):100–123, 1993) has the same type of results with different assumption

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Survival Data with Explanatory Processes: A Full Nonparametric Bayesian Analysise hazard function is considered in the context of nonparametric Bayesian analysis. Particular cases are semi-parametric proportional hazards and multiplicative intensity models. If the baseline predictable hazard function is a Levy process, then its distribution conditionally on the censoring times

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