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. 2021 May 13;71(1):121–138. doi: 10.1093/sysbio/syab037

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© The Author(s) 2021. Published by Oxford University Press on behalf of the Society of Systematic Biologists.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure 3. — The impact of smoothing priors increases with skyline complexity. For the GMRF, we find that for a fixed (ratio of smoothing parameter to total coalescent event count), significantly depends on the complexity, , of our skyline. The colored curves are (along the arrow) for at with as the number of coalescent events per skyline segment. The dashed line depicts the threshold below which the prior contributes more than the coalescent data to posterior estimate precision (asymptotically). For a given tree and , the larger the number of demographic parameters we choose to estimate, the stronger the influence of the prior on those estimates.

Inline graphic — The impact of smoothing priors increases with skyline complexity. For the GMRF, we find that for a fixed (ratio of smoothing parameter to total coalescent event count), significantly depends on the complexity, , of our skyline. The colored curves are (along the arrow) for at with as the number of coalescent events per skyline segment. The dashed line depicts the threshold below which the prior contributes more than the coalescent data to posterior estimate precision (asymptotically). For a given tree and , the larger the number of demographic parameters we choose to estimate, the stronger the influence of the prior on those estimates.