Abstract
A general approach to derive the weak convergence, when centered and rescaled, of certain Bayesian nonparametric priors is proposed. This method may be applied to a wide range of processes including, for instance, nondecreasing nonnegative pure jump Lévy processes and normalized nondecreasing nonnegative pure jump Lévy processes with known finite dimensional distributions. Examples clarifying this approach involve the beta process in latent feature models and the Dirichlet process.
Original language | English |
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Pages (from-to) | 215-229 |
Number of pages | 15 |
Journal | Statistical Methods and Applications |
Volume | 26 |
Issue number | 2 |
DOIs | |
Publication status | Published - Jun 1 2017 |
Externally published | Yes |
Keywords
- Beta process
- Dirichlet process
- Lévy processes
- Nonparametric Bayesian inference
- Processes with independent increments
- Quantile process
- Weak convergence
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty