Bayesian Methods

Overview

Teaching: 180 min
Exercises: 0 min

Questions

• How does a estimating a solution using a Bayesian method differ from likelihood?

• How does the output of a Bayesian tree search differ from that of likelihood or parsimony?

Objectives

• Explain what different models are telling us about evolution.

• How does a heuristic search differ under likelihood?

• Perform ML analyses using PAUP on LONI

##Briefly, let’s finish RAxML

I have placed a Phylip formatted in the classroom repo. Move into the classroom repo, do a git pull, and observe which file has been added. In your copy, make a new folder called “raxmllab” and add this file to its data directory (which you’ll also have to create).

Enter this directory, and load raxml

module load raxml

And execute a tree search

raxmlHPC-PTHREADS-SSE3 -m GTRGAMMA -s Data/primates.phy -n sample -p 869877

You can also run a bootstrap in RAxML:

raxmlHPC-PTHREADS-SSE3 -m GTRGAMMA -s Data/primates.phy -n bootrun -p 9734057 -­b 89723947 -# 100

RAxML does not automatically map the bootstraps to the tree, so we have to do that by hand:

raxmlHPC -m GTRCAT -p 12345 -f b -t RAxML_bestTree.sample -z RAxML_bootstrap.bootrun -n mapped

You can now visualize the tree in IcyTree or FigTree.

Moving on to RevBayes

First, we’ll download [RevBayes] to our LONI work directories.

git clone https://github.com/revbayes/revbayes.git

Now, change into the RevBayes build directory.

cd revbayes/projects/cmake

And execute the build script.

./build.sh

The PDF for today’s tutorial is here

While this downloads, we will discuss the crucial differences between Bayesian inference and maximum likelihood difference. Download this software, for visualizing posterior samples.

In your work directory, create a RevBayesLab0 directory. Change into it.

Key Points