Non‐linear state space modelling of fisheries biomass dynamics by using Metropolis‐Hastings within‐Gibbs sampling

By R. Millar, Renate Meyer. 2000

176 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f7da91d2c684f4ac9d0736aa47a2cbb0583bc7ae

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In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

State-space models for the dynamics of wild animal populations (2004, unknown): “Raftery et al., 1995; Newman, 1998, 2000; Meyer and Millar, 1999; Millar and Meyer, 2000; Trenkel et al., 2000; Besbeas et al., 2002).”
Ecological non-linear state space model selection via adaptive particle Markov chain Monte Carlo (AdPMCMC) (2010, methodology): “Studies that employ the Metropolis-Hastings within Gibbs (MHG) algorithm avoid these restrictive linear assumptions or local linearizations, [Millar and Meyer, 2000, Ward, 2006, Clark and Bjornstad, 2004, Sæther et al., 2007], but the performance of MHG is known to be very sensitive to the process…”
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.”
A Bayesian approach to combining animal abundance and demographic data (2004, methodology): “Non–normality State–space models have been used by many authors to describe ecological processes (Millar & Meyer, 2000; Newman, 1998; Sullivan, 1992; Jamieson & Brooks, 2004).”
Estimating Fisheries Reference Points from Catch and Resilience (2017, methodology): “The BSM was implemented as a Bayesian statespace estimation model (Meyer and Millar 1999; Millar and Meyer 1999), which allowed accounting for variability in both population dynamics (process error) and measurement and sampling (observation error) (Thorson et al. 2014).”
Monte Carlo State-Space Likelihoods by Weighted Posterior Kernel Density Estimation (2004, methodology): “MC state-space research has typically considered methods for single long time series, such as for nancial data (Carlin, Polson, and Stoffer 1992; Shephard and Pitt 1997; Tanizaki and Mariano 1998; Pitt and Shephard 1999; Durham and Gallant 2002) or sheries catch and survey records (Bjørnstad, Fr”
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.”