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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
ISSN:1054-3139
1095-9289
DOI:10.1093/icesjms/fsy044