Accounting for observation noise in equation‐free forecasting: The hidden‐Markov S‐map

Esguerra, Dylan; Munch, Stephan B.

doi:10.1111/2041-210x.14337

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Accounting for observation noise in equation‐free forecasting: The hidden‐Markov S‐map

2024
By Esguerra, Dylan ; Munch, Stephan B.
Source: Methods in Ecology and Evolution, 15(8), 1347-1359

[PDF-4.90 MB]

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Journal Title:

Methods in Ecology and Evolution
Personal Author:

Esguerra, Dylan ; Munch, Stephan B.

Esguerra, Dylan ; Munch, Stephan B. Less -
NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; SWFSC (Southwest Fisheries Science Center)

NMFS (National Marine Fisheries Service) ; SWFSC (Southwest Fisheries Science Center) Less -
Description:

Ecosystems contain numerous species interacting with each other and the environment. However, data on all relevant state variables is rarely available, hampering inference and prediction. Empirical dynamic modelling (EDM) is a valuable tool for prediction, inference and control in such partially observed systems.
Source:

Methods in Ecology and Evolution, 15(8), 1347-1359
DOI:

https://doi.org/10.1111/2041-210x.14337
ISSN:

2041-210X;2041-210X;
Format:

pdf
Publisher:

Wiley
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Journal Article
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https://creativecommons.org/licenses/by-nc-nd/4.0/
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CC BY-NC-ND
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Library
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urn:sha-512:ede9568bf34a545eaf3e21afd089005631405b5325ab42c89721afccc4d34c0976eeedd30ea4d4ae43cfe306a82de14e1997291fc3a3cd7afd824b126e83a8bd
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Accounting for observation noise in equation‐free forecasting: The hidden‐Markov S‐map

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