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Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets.

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Title: Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets.
Authors: Morris, Dylan J.1 (AUTHOR) dylan.morris@adelaide.edu.au, Kennedy, Lauren1 (AUTHOR), Black, Andrew J.1 (AUTHOR)
Source: PLoS Computational Biology. 12/4/2025, Vol. 21 Issue 12, p1-23. 23p.
Database: Academic Search Ultimate
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  Data: <searchLink fieldCode="JN" term="%22PLoS+Computational+Biology%22">PLoS Computational Biology</searchLink>. 12/4/2025, Vol. 21 Issue 12, p1-23. 23p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=189829252
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        Value: 10.1371/journal.pcbi.1013775
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        Text: English
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      – TitleFull: Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets.
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              Text: 12/4/2025
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