The SI on Bayesian Nonparametrics I co-edited together with Antonio Lijoi and Antonietta Mira is closed, with 10 interesting paper accepted.

The aim of this Special Issue is twofold. On one hand, it wishes to give a broad overview of popular models used in BNP, and of the related computational methods for implementation, through a tutorial paper. On the other hand, it focuses on theoretical advances and modern challenging applications of the BNP approach with special emphasis, among others, on: Bayesian asymp-
totics, Bayesian topic models, Bayesian linear and regression models, Bayesian semiparametric state space models, dictionary learning with application to image processing, sensitivity and robustness. This is the list of papers.

  1. Theory and Computations for the Dirichlet Process and Related Models: An Overview, by  Alejandro Jara
  2. Frequentistic approximations to Bayesian prevision of exchangeable random elements, by Emanuele Dolera, Donato M. Cifarelli and Eugenio Regazzini
  3. A Dirichlet Process Functional Approach to Heteroscedastic-Consistent Covariance Estimation, by George Karabatsos
  4. Nonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes, by Kar Wai Lim, Wray Buntine, Changyou Chen and Lan Du
  5. Bayes linear kinematics in a dynamic survival model, by Kevin J. Wilson and Malcolm Farrow
  6. Nonparametric adaptive Bayesian regression using priors with tractable normalizing constants and under qualitative assumptions, by Khader Khadraoui
  7. Robust Identification of Highly Persistent Interest Rate Regimes, by Stefano Peluso, Antonietta Mira and Pietro Muliere
  8. Indian Buffet Process Dictionary Learning : algorithms and applications to image processing, by Hong-Phuong Dang and Pierre Chainais
  9. Bayesian Nonparametric System Reliability using Sets of Priors, by Gero Walter, Louis Aslett and Frank Coolen
  10. Robustness in Bayesian Nonparametrics, by  Sudip Bose

We wish to thank the authors of this special issue for submitting interesting papers, and the reviewers for their time spent on reviewing the above manuscripts.

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