Bayesian state-space modeling of age-structured data: fitting a model is just the beginning

By R. Millar, Renate Meyer. 2000

135 citations recorded by the source corpus.

Subfields: Demography, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/9ae3c28d0f8573172fce5f8a7a313811c023dd38

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Citations in the corpus

POPULATION TIME SERIES: PROCESS VARIABILITY, OBSERVATION ERRORS, MISSING VALUES, LAGS, AND HIDDEN STATES (2004, methodology): “Previous ecological applications of MCMC for simulation of Bayesian posteriors include Bjørnstad et al. (1999), Millar and Meyer (2000), Calder et al. (2003), Clark et al. (2003, 2004), Stenseth et al. (2003), and Wikle (2003).”
ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR (2006, methodology): “Millar and Meyer (2000a) use the Bayesian framework to implement state-space models for fisheries stocks based on the nonlinear Moran-Ricker or Beverton-Holt forms.”
Dealing with data conflicts in statistical inference of population assessment models that integrate information from multiple diverse data sets (2017, background): “The most commonly modelled process is recruitment (Maunder and Deriso 2003), but other processes such as fishing mortality (Nielsen and Burg 2014) or survival (Millar and Meyer 2000) have been modelled.”
Modelling non-stationary natural mortality in catch-at-age models (2012, methodology): “Bayesian methods are computationally feasible for non-stationary time-series models and are effective in solving state–space time-series models and hierarchical models, including fisheries catch-at-age models (Millar and Meyer, 2000; Calder et al., 2003; Carroll et al., 2006).”
Ecological Models and Data in R (2008, background): “A growing number of papers — some in ecology, but largely focused in conservation and management (especially in fisheries) provide example models for specific systems ∗ (Millar and Meyer, 2000; Jonsen et al., 2003; Morales et al., 2004; McCarthy and Parris, 2004; Clarke et al., 2006).”
Remedies for pseudoreplication (2004, methodology): “For example,Meyer and Millar (1999)describe methodology for fitting nonlinear and nonnormal state-space surplus production models, while Millar and Meyer (2000a)provide more detail of the underlying theory.”
IMPROVING INFERENCES IN POPULATION STUDIES OF RARE SPECIES THAT ARE DETECTED IMPERFECTLY (2005, background): “We expect such efforts to combine data from multiple sources to become more common in the near future (see Schnute 1994, Gallucci et al. 1996, Quinn and Deriso 1999, Elliott and Little 2000, Millar and Meyer 2000, Trenkel et al. 2000, Grove et al. 2002, White and Lubow 2002).”
Viable salmonid populations and the recovery of evolutionarily significant units (2000, background): “Milner et al. (1993) explored correlations in salmonid densities between sites within and between tributaries in a river in North Wales.”
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods (2009, methodology): “In doing so, WinBUGS may provide an efficient and flexible tool to fit such models, possibly nonlinear and non Gaussian – as has been realized for several years in fisheries (Meyer and Millar 1999; Millar and Meyer 2000; Rivot and Prévost 2002; Lewy and Nielsen 2003; Rivot et al. 2004).”