TAMING THE BEAST

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It may be tempting to specify the maximum dimension for the model (each group contains only one coalescent event, thus N e N_e N e ​ changes at each branching time in the tree), making it as flexible as possible. This is the parameterization used by the Classic Skyline plot (Pybus et al., 2000), which is the direct ancestor of the Coalescent Bayesian Skyline plot. The choice of the number of dimensions can also have a direct effect on how fast the MCMC converges ( Figure 14). The slower convergence with increasing dimension can be caused by e.g. less information per interval. To some extent it is simply caused by the need to estimate more parameters though. Figure 14: The ESS value of the posterior after running an MCMC chain with 1 0 7 10

If the difference is smaller, you can guess how much the SD estimates must shrink to get a difference that is sufficiently large. Since the SD=sqrt(H/N), we have that N=H/(SD*SD) and H comes from the NS run with a few particles. Run the analysis again, with the increased number of particles, and see if the difference becomes large enough. Press OK to reconstruct the past population dynamics ( Figure 11). Figure 11: Reconstructing the Bayesian Skyline plot in Tracer.It has already been more than two weeks since the second Taming the BEAST workshop took place on Waiheke island in New Zealand. The dataset consists of an alignment of 63 Hepatitis C sequences sampled in 1993 in Egypt (Ray et al., 2000). This dataset has been used previously to test the performance of skyline methods (Drummond et al., 2005; Stadler et al., 2013). In the Partitions panel, import the nexus file with the alignment by navigating to File > Import Alignment in the menu and then finding the hcv.nexus file on your computer or simply drag and drop the file into the BEAUti window.

The difference between the estimates is the way they are estimated from the nested sampling run. Since these are estimates that require random sampling, they differ from one estimate to another. When the standard deviation is small, the estimates will be very close, but when the standard deviations is quite large, the ML estimates can substantially differ. Regardless, any of the reported estimates are valid estimates, but make sure to report them with their standard deviation. How do I know the sub-chain length is large enough? Nested sampling stops automatically when the accuracy in the ML estimate cannot be improved upon. Because it is a stochastic process, some analyses get there faster than others, resulting in different run times. Why are the ESSs so low when I open a log file in Tracer? There are two ways to save the analysis, it can either be saved as a *.pdf for display purposes or as a tab delimited file. replace the point with a new point randomly sampled from the prior using an MCMC chain of subChainLength samples under the condition that the likelihood is at least L min

If the difference is less than 2, the hypotheses may not be distinguishable – in terms of Bayes factors, are barely worth mentioning. Is NS faster than path sampling/stepping stone (PS/SS)? Estimates of N e N_e N e ​ therefore do not directly tell us something about the number of infected, nor the transmission rate. However, changes in N e N_e N e ​ can be informative about changes in the transmission rate or the number of infected (if they do not cancel out). The sequences were all sampled in 1993 so we are dealing with a homochronous alignment and do not need to specify tip dates. To aim for an SD of say 2, we need to run again with N particles such that 2=sqrt(125/N), which means 4=125/N, so N=125/4 and N=32 will do. Note that the computation time of nested sampling is linear in the number of particles, so it will take about 32 times longer to run if we change the particleCount from 1 to 32 in the XML. With an estimated 15-25%, Egypt has the highest Hepatits C prevalence in the world. In the mid 20th century, the prevalence of Hepatitis C increased drastically (see Figure 1 for estimates). We will try to infer this increase from sequence data.