arXiv 31 Mar 2024 · Mathematics — Statistics Theory
arXiv:2404.00784 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rasmussen, C. E. and Williams, C. K. I (2006) Gaussian processes for machine learning | 0.644 | 2 | 2 | 100% |
| 2 | Bishop, C. M (2006) Pattern recognition and machine learning | 0.511 | 2 | 1 | 100% |
| 3 | Bardhi, A (2024) Attributes: Selective Learning and Influence | 0.405 | 1 | 1 | 100% |
| 4 | Callander, S (2011) Searching and Learning by Trial and Error | 0.405 | 1 | 1 | 100% |
| 5 | Carnehl, C. and Schneider, J (2023) A Quest for Knowledge | 0.405 | 1 | 1 | 100% |
| 6 | Davies, B (2024) Learning about a changing state self | 0.405 | 1 | 1 | 100% |
| 7 | DeGroot, M. H (2004) Optimal Statistical Decisions | 0.405 | 1 | 1 | 100% |
| 8 | Karatzas, I. and Shreve, S. E (1988) Brownian Motion and Stochastic Calculus, volume 113 of Graduate Texts in Mathematics | 0.405 | 1 | 1 | 100% |
Showing the top 8 of 8 scored citations.