TAMING THE BEAST

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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. 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). 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?

Get to know the advantages and disadvantages of the Coalescent Bayesian Skyline Plot and the Birth-Death Skyline. 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 June this year we organised the first Taming the BEAST workshop, surrounded by the Swiss Alps, in Engelberg, Switzerland. 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? 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 Once the analyses have run, open the log file in Tracer and compare estimates and see whether the analyses substantially differ. You can also compare the trees in DensiTree. To get more accurate estimates, the number of particles can be increased. The expected SD is sqrt(H/N) where N is the number of particles and H the information. The information H is conveniently estimated in the nested sampling run as well.

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). In this tutorial we will estimate the dynamics of the Egyptian Hepatitis C epidemic from genetic sequence data collected in 1993.Marginal likelihood: -12417.389793288146 sqrt(H/N)=(1.9543337689486355)=?=SD=(1.9614418034828585) Information: 122.2214553744953 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. 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. 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