TAMING THE BEAST

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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)? Thanks also to all the speakers for agreeing to take part and making the long journey to New Zealand! The workshop organisers and participants outside of the London School of Hygiene and Tropical Medicine.

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. 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.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 In June this year we organised the first Taming the BEAST workshop, surrounded by the Swiss Alps, in Engelberg, Switzerland.

SCOTTI Tutorial: NEW Reconstruct transmission trees using within-host data with an approximate structured coalescent. We can leave the rest of the priors as they are and save the XML file. We want to shorten the chain length and decrease the sampling frequency so the analysis completes in a reasonable time and the output files stay small. (Keep in mind that it will be necessary to run a longer chain for parameters to mix properly). If we compare the estimates of the population dynamics using different dimensions, we see that most of the dynamics are already captured with having only 2 dimensions, as shown in Figure 13. Adding more dimensions only changes the inferred effective population size before 1900. Note that adding more dimensions adds a slight dip before the increase in the effective population size (around 1900). When comparing to the HPD intervals ( Figure 12) we see that this dip is not significant and may not be indicative of a real decrease in the effective population size before the subsequent increase. Figure 13: Estimated mean effective population sizes using different dimensions.

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So, the main parameters of the algorithm are the number of particles N and the subChainLength. N can be determined by starting with N=1 and from the information of that run a target standard deviation can be determined, which gives us a formula to determine N (as we will see later in the tutorial). The subChainLength determines how independent the replacement point is from the point that was saved, and is the only parameter that needs to be determined by trial and error – see FAQ for details. Bayesian model selection is based on estimating the marginal likelihood: the term forming the denominator in Bayes formula. This is generally a computationally intensive task and there are several ways to estimate them. Here, we concentrate on nested sampling as a way to estimate the marginal likelihood as well as the uncertainty in that estimate. 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). Set the dimension of bPopSizes and bGroupSizes to 4 (the default value is 5) after expanding the boxes for the two parameters ( Figure 8). Figure 7: Show the initialization panel. In this tutorial we will estimate the dynamics of the Egyptian Hepatitis C epidemic from genetic sequence data collected in 1993.

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 BEAUti will recognize the sequences from the *.nexus file as nucleotide data. It will do so for sequence files with the character set of A C G T N, where N indicates an unknown nucleotide. As soon as other non-gap characters are included (e.g. using R or Y to indicate purines and pyramidines) BEAUti will not recognize the data as nucleotides anymore (unless the type of data is specified in the *.nexus file) and open a dialogue box to confirm the data type.

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? 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). 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. 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.