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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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An approximate Bayesian computation approach to parameter estimation in a stochastic stage-structured population

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    Approximate Bayesian computation (ABC) offers a solution for complex population model estimation. This study successfully applied an ABC SMC sampler and a novel method for selecting summary statistics to improve parameter estimation in stage-structured population models.

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    Area of Science:

    • Ecology
    • Computational Biology
    • Population Dynamics

    Background:

    • Complex population models are often analytically intractable for parameter estimation.
    • Approximate Bayesian computation (ABC) is a method for parameter estimation when likelihoods are unavailable.
    • Selecting appropriate summary statistics and distance metrics is crucial for ABC algorithm performance.

    Purpose of the Study:

    • To apply a sequential Monte Carlo ABC sampler (ABC SMC) for parameter estimation in a complex, stochastic, stage-structured population model.
    • To develop and evaluate a systematic method for selecting summary statistics and distance metrics using simulated data and ROC curves.
    • To assess the performance of the ABC SMC approach for realistic population models with individual variation.

    Main Methods:

    • Utilized a sequential Monte Carlo ABC sampler (ABC SMC) for parameter estimation.
    • Developed a systematic approach for choosing summary statistics and distance metrics based on classification theory (ROC curves).
    • Applied the ABC SMC to a simulated stage-structured population model with heterogeneity in development and mortality.

    Main Results:

    • The ABC SMC sampler was successfully applied to estimate parameters in a complex population model.
    • The systematic method for selecting summary statistics and distance metrics proved effective.
    • The evaluations indicated promising performance for model inference in realistic stage-structured populations.

    Conclusions:

    • The ABC SMC approach is a viable and promising method for parameter estimation in complex population models.
    • The developed method for selecting summary statistics enhances the accuracy and efficiency of ABC analyses.
    • This approach facilitates more robust inferences for stage-structured population dynamics incorporating individual variation.