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Copy file name to clipboardexpand all lines: README.md
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@@ -13,6 +13,8 @@ subtitle: Prior selection and clock calibration using Influenza A data.
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# Background
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In the Bayesian analysis of sequence data, priors play an important role. When priors are not specified correctly, it may cause runs to take a long time to converge, not converge at all or cause a bias in the inferred trees and model parameters. Specifying proper priors and starting values is crucial and can be a difficult exercise in the beginning. It is not always easy to pick a proper model of tree generation (tree prior), substitution model, molecular clock model or the prior distribution for an unknown parameter.
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<figcaption>Figure 5: Specifying tip dates.</figcaption>
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</figure>
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<br>
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You should now see that the tip ages have been filled in for all of the taxa with the **Date (raw value)** columns showing the date strings extracted from the taxon names, and the **Age/Height** column showing numbers on the order of 0.1 (the age in years of each tip relative to the most recent sample).
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You should now see that the tip ages have been filled in for all of the taxa with the **Date (raw value)** columns showing the date strings extracted from the taxon names, and the **Age/Height** column showing numbers on the order of 0.1 (the age in years of each tip relative to the most recent sample). If you did everything correctly, the sequence with the _most recent_ sampling date (2009/06/08) should have the samllest height (0.0 in this case).
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Now we are done with the data specification and we are about to start specifying models and priors for the model parameters.
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<figcaption>Figure 16: Specifying the equilibrium nucleotide frequencies prior.</figcaption>
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<figcaption>Figure 16: Specifying the equilibrium nucleotide frequencies prior. As it is a multivariate distribution, BEAUti cannot plot it.</figcaption>
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