March 31, 2024Open Access

Estimating sample paths of Gauss-Markov processes from noisy data

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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.

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Benjamin Davies (Sun,) studied this question.

synapsesocial.com/papers/68e718f1b6db64358769252b https://doi.org/https://doi.org/10.48550/arxiv.2404.00784

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