Do higher-order moments improve inference of population dynamics?
Fitting mathematical models of population dynamics to microbial time-series data allows us to estimate the ecological processes and interactions taking place in the microbiome. Repeated experiments of microbial systems yield replicates which slightly differ from each other. Some of this variability arises due to the fact that births and deaths occur at random. Most prior work focuses on fitting a…
Mathematical models of population dynamics are utilized for estimating ecological processes and interactions within microbial communities. When repeated experiments are conducted on microbial systems, the results often vary slightly from one another. This variation is partly due to random occurrences of births and deaths. Prior research has primarily concentrated on fitting deterministic models to the average across these replicates.
However, the authors of this study employ a stochastic model to fit the variability observed across replicates. They then delve into the conditions that facilitate the inference of a greater proportion of ecological parameters accurately.
By employing a simulation-driven approach, the researchers investigate the circumstances that enable their method to correctly infer a larger number of ecological parameters. They find that their approach results in a substantial improvement in parameter inference. Furthermore, their Bayesian approach not only allows for the incorporation of prior knowledge about the system but also provides a distribution of parameters. This distribution offers insights into the uncertainty associated with the estimates.
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