TAMING THE BEAST

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This sets the number of segments equal to 4 (the parameter dimension), which means N e N_e N e ​ will be allowed to change 3 times between the tMRCA and the present (if we have d d d segments, N e N_e N e ​ is allowed to change d − 1 d-1 d − 1 times). If there are any further issues, please raise them on the Github repository of the tutorial in question. Marginal likelihood: -12426.207750474812 sqrt(H/N)=(1.8913059067381148)=?=SD=(1.8374367294317693) Information: 114.46521705159945 The idea of holding a BEAST 2 workshop has been brewing for a while, motivated by the need for a Bayesian phylogenetics workshop that is focused on BEAST 2 and facilitates exchanges between developers and (both current and future) BEAST 2 users. The parallel implementation makes it possible to run many particles in parallel, giving a many-particle estimate in the same time as a single particle estimate (PS/SS can be parallelised by steps as well). The output is written on screen, which I forgot to save. Can I estimate them directly from the log files?

In practice, we can get away much smaller sub-chain lengths, which you can verify by running multiple NS analysis with increasing sub-chain lengths. If the ML and SD estimates do not substantially differ, you know the shorter sub-chain length was sufficient. How many particles do I need?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. 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 output will have the years on the x-axis and the effective population size on the y-axis. By default, the y-axis is on a log-scale. If everything worked as it is supposed to work you will see a sharp increase in the effective population size in the mid 20th century, similar to what is seen on Figure 12. We hope that the community will play an active role in curating the tutorials, either by updating or correcting existing tutorials, or by contributing new tutorials. In June this year we organised the first Taming the BEAST workshop, surrounded by the Swiss Alps, in Engelberg, Switzerland.

Marginal likelihood: -12428.480923049345 sqrt(H/N)=(11.220392192278625)=?=SD=(11.491864352217954) Information: 125.89720094854714 Because we shortened the chain most parameters have very low ESS values. If you like, you can compare your results with the example results we obtained with identical settings and a chain of 30,000,000 ( hcv_coal_30M.log).

The exported file will have five rows, the time, the mean, median, lower and upper boundary of the 95% HPD interval of the estimates, which you can use to plot the data with other software (R, Matlab, etc). Choosing the Dimension There are descendants of the coalescent skyline in BEAST that either estimate the number of segments (Extended Bayesian Skyline (Heled & Drummond, 2008)) or do not require the number of segments to be specified (Skyride (Minin et al., 2008)), but instead makes very strong prior assumptions about changes in N e N_e N e ​ . Exploring the results of the Coalescent Bayesian Skyline analysis