Estimating sample paths of Gauss-Markov processes from noisy data
Statistics Theory
2024-04-02 v1 Econometrics
Probability
Statistics Theory
Abstract
I derive the pointwise conditional means and variances of an arbitrary Gauss-Markov process, given noisy observations of points on a sample path. These moments depend on the process's mean and covariance functions, and on the conditional moments of the sampled points. I study the Brownian motion and bridge as special cases.
Cite
@article{arxiv.2404.00784,
title = {Estimating sample paths of Gauss-Markov processes from noisy data},
author = {Benjamin Davies},
journal= {arXiv preprint arXiv:2404.00784},
year = {2024}
}