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Abstract:
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A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing due to imperfect detection of individuals, the associated measurement error can be accommodated under certain study designs (e.g., those that involve multiple surveys or replication). However, the interaction of the measurement error and the underlying dynamic process can complicate the implementation of these statistical agent-based models for population demography. In a Bayesian setting, traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we combine an emulation approach and multi-stage recursive Bayesian computing to fit models in such a way that avoids the necessity of checking a complicated set of conditions repeatedly. Our approach is intuitive and more accessible for practitioners than traditional approaches and can be parallelized easily for additional computational efficiency.
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