The dynamics of a population exhibiting exponential growth can be modelled as a birth-death process, which naturally captures the stochastic variation in population size over time.  The genealogy of individuals sampled at the present time is then described by the reversed reconstructed process (RRP), which traces the ancestry of the sample backwards from the present. We show that a simple, analytic, time rescaling of the RRP provides a straightforward way to derive its inter-event times. The same rescaling characterises other distributions underlying this process, obtained elsewhere in the literature via more cumbersome calculations. We also consider the case of incomplete sampling of the population, in which each leaf of the genealogy is retained with an independent Bernoulli trial  , and we show that corresponding results for Bernoulli-sampled RRPs can be derived using time rescaling, for any values of the underlying parameters. We derive a scaling limit as the sampling probability goes to 0, corresponding to the underlying population growing to infinity  . We show that in this setting, the process has two different time scales: the time to the first event grows to infinity, but the inter-event times nearer the root of the tree are approximately exponentially distributed.
The dynamics of a population exhibiting exponential growth can be modelled as a birth-death process, which naturally captures the stochastic variation in population size over time. In this article, we consider a supercritical birth-death process, started at a random time in the past, and conditioned to have n sampled individuals at the present. The genealogy of individuals sampled at the present time is then described by the reversed reconstructed process (RRP), which traces the ancestry of the sample backwards from the present. We show that a simple, analytic, time rescaling of the RRP provides a straightforward way to derive its inter-event times. The same rescaling characterises other distributions underlying this process, obtained elsewhere in the literature via more cumbersome calculations. We also consider the case of incomplete sampling of the population, in which each leaf of the genealogy is retained with an independent Bernoulli trial with probability \psi , and we show that corresponding results for Bernoulli-sampled RRPs can be derived using time rescaling, for any values of the underlying parameters. A central result is the derivation of a scaling limit as \psi approaches 0, corresponding to the underlying population growing to infinity , using the time rescaling formalism . We show that in this setting, after a linear time rescaling, the event times are the order statistics of n logistic random variables with mode \log(1/\psi); moreover, we show that the inter-event times are approximately exponentially distributed.
A
In this article, we consider a supercritical birth-death process, started at a random time in the past, and conditioned to have n sampled individuals at the present.
A
with probability \psi
R
We derive
A central result is the derivation of
R
the sampling probability goes to
\psi approaches
A
, using the time rescaling formalism
R
the process has two different time scales: the time to the first event grows to infinity, but the inter-event times nearer the root of
after a linear time rescaling,
R
tree
event times are the order statistics of n logistic random variables with mode \log(1/\psi); moreover, we show that the inter-event times