Author: de Silva, Eric; Ferguson, Neil M.; Fraser, Christophe
Title: Inferring pandemic growth rates from sequence data Document date: 2012_8_7
ID: 1piyoafd_43
Snippet: The Bayesian skyline assumes that N e is autocorrelated through time and so smooths the vector representing the effective population size in each grouped interval [32] . We also experimented with varying the number of intervals (exploring group sizes of 5, 10, 20 and 30) but found very little difference in the BSPs produced (results not shown). While here we fixed the number of grouped intervals a priori, other methods use the reversible jump MCM.....
Document: The Bayesian skyline assumes that N e is autocorrelated through time and so smooths the vector representing the effective population size in each grouped interval [32] . We also experimented with varying the number of intervals (exploring group sizes of 5, 10, 20 and 30) but found very little difference in the BSPs produced (results not shown). While here we fixed the number of grouped intervals a priori, other methods use the reversible jump MCMC sampling [33] . Pandemic growth rates from sequences E. de Silva et al. 1803
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