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A Bayesian approach to identifying and compensating for model misspecification in population models

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James Thorson, Steve Munch, and Kotaro Ono, 2014, A Bayesian approach to identifying and compensating for model misspecification in population models: Ecology, v. 95, iss. 2, 329–341 p.

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State-space estimation methods are increasingly used in ecology to estimate productivity and abundance of natural populations while accounting for variability in both population dynamics and measurement processes. However, functional forms for population dynamics and density dependence often will not match the true biological process, and this may degrade the performance of state-space methods. We therefore develop a Bayesian semi-parametric state-space model, which uses a Gaussian process (GP) to approximate the population growth function. This offers two benefits for population modeling. First, it allows data to update a specified 'prior' on the population growth function, while reverting to this prior when data are uninformative. [...]

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  • John Wesley Powell Center for Analysis and Synthesis

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DOI https://www.sciencebase.gov/vocab/term/528e9a2ce4b05d51c7038afe 10.1890/13-0187.1

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citationTypeJournal
journalEcology
noteThorson, J., Munch, S., and Ono, K. (2014). A Bayesian approach to identifying and compensating for model misspecification in population models. Ecology, 95(2), 329–341. doi: 10.1890/13-0187.1
parts
typeVolume
value95
typeIssue
value2
typePages
value329–341

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