At the top of the window is an option called , specifies how often each operator is applied relative to each other.Some parameters have very little interaction with the rest of the model and as a result tend to be estimated very efficiently - an example is the Firstly we have the Length of chain.This tutorial was written by Tracy Heath for workshops on applied phylogenetics and molecular evolution and is licensed under a Creative Commons Attribution 4.0 International License.
The default value of 10,000,000 is entirely arbitrary and should be adjusted according to the size of your dataset.
In order examine whether a particular chain length is adequate, the resulting log file can be analysed using Tracer.
Some operators don’t have any tuning settings so have n/a under this column.
Changing the tuning setting will set how large a move that operator will make which will affect how often that change is accepted by the MCMC which will affect the efficency of the analysis.
If you know what the HKY model and the gamma model of rate heterogeneity are then you should be OK.
You should also be familiar with at least the basics of Bayesian inference and Markov chain Monte Carlo sampling. BEAUti is an interactive graphical application for designing your analysis and generating the control file (a BEAST XML file) which BEAST will use to run the analysis.The operators specify how the parameter changes as the MCMC runs.This table lists the parameters, their operators and the tuning settings for these operators. These will be called things like operator simply picks a new value uniformally within a range.As we have sequence data from a handful of species, we will select the 213: 402-420 is the simplest model of speciation where each lineage is assumed to have speciated at a fixed rate.The model has a single parameter, the ‘birth rate’ of new species.The next stage is to look at the operators for the MCMC.