Nonparametric estimation of diffusions: a differential equations approach

75 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 Bayesian posterior contraction rates for discretely observed scalar diffusions (2015, background): “1 of [20], where a certain Gaussian prior is chosen for the drift function b, while the diffusion coefficient is modelled parametrically.”
Analysis of the Gibbs Sampler for Hierarchical Inverse Problems (2013, background): “Our theory on the slowing down of the δ-chain can be extended to cover nonlinear Gaussian-conjugate Bayesian inverse problems and in particular the nonparametric drift estimation in SDE’s setting considered in [31, 35, 30]; see [2, Chapter 4.”
Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions (2018, methodology): “The Bayesian interpretation of b̂T is exploited in our proofs, and is further appealing since it comes hand in hand with uncertainty quantification methodology (‘posterior credible sets’), and posterior sampling is in principle feasible even for ‘real-world’ discrete data by simulation techniques, s”
Posterior consistency via precision operators for Bayesian nonparametric drift estimation in SDEs (2012, unknown): “This numerical approach, including algorithms to accommodate both continuously and discretely observed data, is detailed in the paper [26].”
Bayesian inference with optimal maps (2011, unknown): “with appropriate boundary conditions [56, 57].”