Hidden birth event information restores identifiability to time-dependent birth-death phylodynamic models; mutation-at-birth models make sequences sufficient to recover it.
(2010) Sampling-through-time in birth-death trees
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SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.
Markovian population models induce unique genealogy processes whose exact likelihoods are given by model-determined filter equations, generalizing prior phylodynamic methods.
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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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Rates of forgetting for the sequentially Markov coalescent
SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.
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Exact phylodynamic likelihood via structured Markov genealogy processes
Markovian population models induce unique genealogy processes whose exact likelihoods are given by model-determined filter equations, generalizing prior phylodynamic methods.