Fundamentals of Nonparametric Bayesian Inference

773 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions (2018, methodology): “3 in [21] with the contraction rate from Theorem 1, we deduce that ‖b̂T − b0‖μ0 = OPb0 (MT εT ) as T → ∞.”
Martingale posterior distributions (2021, methodology): “Hahn et al. (2018) suggest the form αi = (i + 1)−1 inspired from the stick-breaking process of the posterior DP as in the Bayesian bootstrap, which works well for estimating pn(y) but we find this performs poorly when predictive resampling, giving too little uncertainty.”
Consistency of mixture models with a prior on the number of components (2022, methodology): “…have been made in establishing posterior consistency and contraction rates for density estimation when using nonparametric mixture models (see Ghosal and Van der Vaart, 2017 and the many references therein) or finite mixture models with a prior on the number of components (Kruijer et al.,…”
Bayesian Learning with Wasserstein Barycenters (2018, methodology): “We refer the reader to [17, 23], and references therein, for detailed accounts on posterior consistency, a highly desirable feature of a Bayesian estimation procedure, both from a semi-frequentist perspective as well as from the “merging of opinions” point of view on Bayesian statistics (cf.”