Inference for dynamic and latent variable models via iterated, perturbed Bayes maps

Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process.

Approximating Variance of Demographic Parameters Using the Delta Method: A Reference for Avian Biologists

Avian biologists routinely estimate sampling variance for parameter estimates such as daily nest survival, fecundity, annual survival, and density. However, many biologists are not certain of methods to derive sampling variance for parameters when survival rates change temporal scales. Similar methods are needed to obtain sampling variance when biologists combine parameter estimates to calculate an indirect demographic parameter, such as population growth rate. The delta method is a useful technique for approximating sampling variance when the desired demographic parameter is a function of at least one other demographic parameter. However, the delta method is rarely taught in most graduate-level biology or ecology courses, and application of this method may be discouraged by seemingly daunting formulas in reference books. Here, I provide five examples of sampling variance approximations for common situations encountered by avian ecologists, with step-by-step explanations of the equations involved.