TAMING THE BEAST

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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? Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. Marginal likelihood: -12428.480923049345 sqrt(H/N)=(11.220392192278625)=?=SD=(11.491864352217954) Information: 125.89720094854714 Marginal likelihood: -12428.557546706481 sqrt(H/N)=(11.22272275528845)=?=SD=(11.252847709777592) Information: 125.94950604206919

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. 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).

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However, the only informative events used by the Coalescent Bayesian Skyline plot are the coalescent events. Thus, using a maximally-flexible parameterization with only one informative event per segment often leads to erratic and noisy estimates of N e N_e N e ​ over time (especially if segments are very short, see Figure 6). Grouping segments together leads to smoother and more robust estimates. Thanks also to all the speakers for agreeing to take part and making the long journey to New Zealand!

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.Navigate to Analysis > Bayesian Skyline Reconstruction. From there open the *.trees file. To get the correct dates in the analysis we should specify the Age of the youngest tip. In our case it is 1993, the year where all the samples were taken. If the sequences were sampled at different times (heterochronous data), the age of the youngest tip is the time when the most recent sample was collected. This depends on many things, but in general, depends on how accurate the estimates should be. For NS, we get an estimate of the SD, which is not available for PS/SS. If the hypotheses have very large differences in MLs, NS requires very few (maybe just 1) particle, and will be very fast. If differences are smaller, more particles may be required, and the run-time of NS is linear in the number of particles. 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

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 We will be using R to analyze the output of the Birth-Death Skyline plot. RStudio provides a user-friendly graphical user interface to R that makes it easier to edit and run scripts. (It is not necessary to use RStudio for this tutorial). Navigate to the Priors panel and select Coalescent Bayesian Skyline as the tree prior ( Figure 5). Figure 5: Choose the Coalescent Bayesian Skyline as a tree prior. Marginal likelihood: -12426.207750474812 sqrt(H/N)=(1.8913059067381148)=?=SD=(1.8374367294317693) Information: 114.46521705159945

We also identified some issues with a few of the tutorials during the workshop and I’ll also be updating them soon as well. Since tutorials are stored on GitHub, it is easy for anyone in the community to contribute to Taming the BEAST. If there are any further issues, please raise them on the Github repository of the tutorial in question.