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About this paper
Evan Sidrow, Alexandre Bouchard-Côté, Lloyd T. Elliott
VIPR estimates uncertainty over ultrametric evolutionary trees by sampling pairwise taxon times and clustering them with single-linkage. It derives a differentiable density for resulting trees, enabling gradient-based inference across tree topologies without MCMC in optimization. On DS1, its marginal-log-likelihood gap was −2.29 nats against an estimated reference, versus −0.53 for VBPI10; on COVID-19, VIPR’s MLL results were substantially worse than VBPI’s.
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