Consistent nonparametric Bayesian inference for discretely observed scalar diffusions
classification
🧮 math.ST
stat.TH
keywords
conditionsnonparametricpriorsbayesbayesianconcreteconsistencyconsistent
read the original abstract
We study Bayes procedures for the problem of nonparametric drift estimation for one-dimensional, ergodic diffusion models from discrete-time, low-frequency data. We give conditions for posterior consistency and verify these conditions for concrete priors, including priors based on wavelet expansions.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.