Summary: | In very simple terms, a Bayesian analysis involves drawing estimatable parameter values from some prior distribution, computing population dynamics and assigning a likelihood value to each combination based on comparisons to data containing information on population size and/or trend. A posterior distribution may then be constructed and conclusions drawn about the parameter estimates. In Model Ia (see Appendix) r B1 , r B2 , ( ) 1 arg ~ ln B Nt , ( ) 2 arg ~ ln B Nt are the parameter values drawn from priors for the intrinsic growth rate and the log of the recent abundance for the two populations under consideration.
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