Hidden birth event information restores identifiability to time-dependent birth-death phylodynamic models; mutation-at-birth models make sequences sufficient to recover it.
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NBFFG combines a closed-form backward filter from a linear-Gaussian proxy process with a learned neural residual to enable efficient variational inference and unbiased pathwise subsampling for nonlinear diffusions on trees.
BEAGLE 4.1 adds gradient evaluation algorithms and hardware-specific implementations for CPUs and GPUs, reporting up to 10-fold speedups for codon models and 4-fold for multi-core CPUs over single-threaded baselines.
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Information on hidden birth events restores identifiability in phylodynamic inference
Hidden birth event information restores identifiability to time-dependent birth-death phylodynamic models; mutation-at-birth models make sequences sufficient to recover it.
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BEAGLE 4.1: A high-performance library for computation on phylogenetic trees across diverse parallel architectures
BEAGLE 4.1 adds gradient evaluation algorithms and hardware-specific implementations for CPUs and GPUs, reporting up to 10-fold speedups for codon models and 4-fold for multi-core CPUs over single-threaded baselines.