Journal article

Approximate Bayesian inference for complex ecosystems.

Michael PH Stumpf

F1000Prime Rep | Published : 2014

Abstract

Mathematical models have been central to ecology for nearly a century. Simple models of population dynamics have allowed us to understand fundamental aspects underlying the dynamics and stability of ecological systems. What has remained a challenge, however, is to meaningfully interpret experimental or observational data in light of mathematical models. Here, we review recent developments, notably in the growing field of approximate Bayesian computation (ABC), that allow us to calibrate mathematical models against available data. Estimating the population demographic parameters from data remains a formidable statistical challenge. Here, we attempt to give a flavor and overview of ABC and its..

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University of Melbourne Researchers

Grants

Awarded by Biotechnology and Biological Sciences Research Council


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