Nonparametric Bayesian methods for one-dimensional diffusion models

Abstract

In this paper we review recently developed methods for nonparametric Bayesian inference for one-dimensional diffusion models. We discuss different possible prior distributions, computational issues, and asymptotic results.

References in corpus (3)

• Rates of contraction of posterior distributions based on Gaussian process priors
• Reversible jump MCMC for nonparametric drift estimation for diffusion processes
• Consistent nonparametric Bayesian inference for discretely observed scalar diffusions

Cited by in corpus (2)

• Nonparametric Bayesian posterior contraction rates for discretely observed scalar diffusions
• A Bayesian approach to regional decadal predictability: Sparse parameter estimation in high-dimensional linear inverse models of high-latitude sea surface temperature variability