TAMING THE BEAST

Product Information

To change the number of segments we have to navigate to the Initialialization panel, which is by default not visible. Navigate to View > Show Initialization Panel to make it visible and navigate to it ( Figure 7). NS works in theory if and only if the points generated at each iteration are independent. If you already did an MCMC run and know the effective sample size (ESS) for each parameter, to be sure every parameter in every sample is independent you can take the length of the MCMC run divided by the smallest ESS as sub-chain length. This tend to result in quite large sub-chain lengths. 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)? The Coalescent Bayesian Skyline model allows N e N_e N e ​ to change over time in a nonparametric fashion (i.e. we do not have to specify an equation governing changes in N e N_e N e ​ over time). Another way to think about the model is as maximally-parameterized, since it infers d d d change-point times (segment boundaries) and a value for N e N_e N e ​ in each segment. This makes the Bayesian Skyline flexible enough to model very complicated N e N_e N e ​ dynamics, provided that enough segments are specified.

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). where the argument after N is the particleCount you specified in the XML, and xyz.log the trace log produced by the NS run. Why are some NS runs longer than others? 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. 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. Get to know the advantages and disadvantages of the Coalescent Bayesian Skyline Plot and the Birth-Death Skyline.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.

Choosing the dimension for the Bayesian Skyline can be rather arbitrary. If the dimension is chosen too low, not all population size changes are captured, but if it is chosen too large, there may be too little information in a segment to support a robust estimate. When trying to decide if the dimension is appropriate it may be useful to consider the average number of informative (coalescent) events per segment. (A tree of n n n taxa has n − 1 n-1 n − 1 coalescences, thus N e N_e N e ​ in each segment is estimated from on average n − 1 d \frac{n-1}{d} d n − 1 ​ informative data points). Would this number of random samples drawn from a hypothetical distribution allow you to accurately estimate the distribution? If not, consider decreasing the dimension. 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.

Sign up to our newsletter

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? The Coalescent Bayesian Skyline divides the time between the present and the root of the tree (the tMRCA) into segments, and estimates a different effective population size ( N e N_e N e ​ ) for each segment. The endpoints of segments are tied to the branching times (also called coalescent events) in the tree ( Figure 6), and the size of segments is measured in the number of coalescent events included in each segment. The Coalescent Bayesian Skyline groups coalescent events into segments and jointly estimates the N e N_e N e ​ ( bPopSizes parameter in BEAST) and the size of each segment ( bGroupSizes parameter). To set the number of segments we have to change the dimension of bPopSizes and bGroupSizes (note that the dimension of both parameters always has to be the same). Note that the length of a segment is not fixed, but dependent on the timing of coalescent events in the tree ( Figure 6), as well as the number of events contained within a segment ( bGroupSizes). Figure 6: Example tree where the red dotted lines show the time-points of coalescent events. 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 there are any further issues, please raise them on the Github repository of the tutorial in question.

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.

Say, we have two models, M1 and M2, and estimates of the (log) marginal likelihood, ML1 and ML2, then we can calculate the Bayes factor, which is the fraction BF=ML1/ML2 (or in log space, the difference log(BF) = log(ML1)-log(ML2)). If BF is larger than 1, model M1 is favoured, and otherwise M2 is favoured. How much it is favoured can be found in the following table (Kass & Raftery, 1995): Figure 1: Bayes factor support.

For the reconstruction of the population dynamics, we need two files, the *.log file and the *.trees file. The log file contains the information about the group sizes and population sizes of each segment, while the trees file is needed for the times of the coalescent events. SCOTTI Tutorial: NEW Reconstruct transmission trees using within-host data with an approximate structured coalescent. 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?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. 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. In June this year we organised the first Taming the BEAST workshop, surrounded by the Swiss Alps, in Engelberg, Switzerland.