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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:ICES journal of marine science Ročník 75; číslo 5; s. 1553 - 1559
Hlavní autor: Subbey, Sam
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford University Press 01.10.2018
Témata:
local minima
automatic differentiation (AD)
likelihood
optimization
global optimum
uncertainty
Hessian
parameters
positive definite
objective function
ISSN:1054-3139, 1095-9289
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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