Parameter estimation in stock assessment modelling: caveats with gradient-based algorithms

Abstract Using simple illustrative examples, this note highlights some of the caveats with gradient-based algorithms. This class of algorithms underpins the state-of-the-art modelling platform in fisheries science. The goal is to sound a cautionary note about an increasing trend in fisheries science...

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Published in:ICES journal of marine science Vol. 75; no. 5; pp. 1553 - 1559
Main Author: Subbey, Sam
Format: Journal Article
Language:English
Published: Oxford University Press 01.10.2018
Subjects:
local minima
automatic differentiation (AD)
likelihood
optimization
global optimum
uncertainty
Hessian
parameters
positive definite
objective function
ISSN:1054-3139, 1095-9289
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Abstract Abstract Using simple illustrative examples, this note highlights some of the caveats with gradient-based algorithms. This class of algorithms underpins the state-of-the-art modelling platform in fisheries science. The goal is to sound a cautionary note about an increasing trend in fisheries science, where blind faith is being invested in results obtained from algorithms that are fast, and proven to have machine precision.
AbstractList Abstract Using simple illustrative examples, this note highlights some of the caveats with gradient-based algorithms. This class of algorithms underpins the state-of-the-art modelling platform in fisheries science. The goal is to sound a cautionary note about an increasing trend in fisheries science, where blind faith is being invested in results obtained from algorithms that are fast, and proven to have machine precision.
Using simple illustrative examples, this note highlights some of the caveats with gradient-based algorithms. This class of algorithms underpins the state-of-the-art modelling platform in fisheries science. The goal is to sound a cautionary note about an increasing trend in fisheries science, where blind faith is being invested in results obtained from algorithms that are fast, and proven to have machine precision.
Author Subbey, Sam
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CitedBy_id crossref_primary_10_1016_j_ecolmodel_2019_108822
crossref_primary_10_1016_j_fishres_2021_105959
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Copyright International Council for the Exploration of the Sea 2018. All rights reserved. For permissions, please email: journals.permissions@oup.com 2018
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Issue 5
Keywords local minima
automatic differentiation (AD)
likelihood
optimization
global optimum
uncertainty
Hessian
parameters
positive definite
objective function
Language English
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Snippet Abstract Using simple illustrative examples, this note highlights some of the caveats with gradient-based algorithms. This class of algorithms underpins the...
Using simple illustrative examples, this note highlights some of the caveats with gradient-based algorithms. This class of algorithms underpins the...
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